AGLDUSDT Backtest - LongTimeLong (-21.95%)

Detailed backtest report for AGLDUSDT using the LongTimeLong strategy over 2025-01-01 to 2025-12-31. Return -21.95% across 68,077 closed trades, 100.0% closed-trade win rate (excludes 736 open orders).

AGLDUSDT * LongTimeLong

Net loss of -21.95% - this configuration did not perform well in the tested window. Max drawdown reached -23.69%, so the equity path was noticeable - there was a meaningful drawdown along the way.

AGLDUSDT | 3LongTimeLong.json | 2025-01-01 - 2025-12-31 | -21.95% | 68,077 closed trades | 100.0% closed WR | 736 open orders

Headline Performance

Data source - GET /backtests/{id} + /equity + /trades * Fields used: return_pct, final_value, total_profit, win_rate, fulfilled_trades, active_orders, unrealized_pnl_exit_net, max_profit, min_profit

This backtest ended with 736 open orders carrying -2,540.32 USDT of unrealized P&L. Headline numbers below split realized (closed trades) from unrealized (mark-to-market on open positions) - only Portfolio PnL ties back to final_value - start_balance.

Return

-21.95%

Final Value

7,805.07 USDT

Portfolio PnL

-2,194.93 USDT (realized + unrealized)

Realized Profit

+516.31 USDT (closed trades only)

Unrealized PnL

-2,540.32 USDT (open positions, mark-to-market)

Win Rate (closed)

100.0% (excludes 736 open orders)

Closed Trades

68,077

Open Orders

736

Best Closed Trade

+0.0175 USDT

Worst Closed Trade

+0.0050 USDT

TL;DR

The LongTimeLong strategy on AGLDUSDT returned -21.95% over 2025-01-01 to 2025-12-31, turning 10,000.00 USDT into 7,805.07 USDT. It executed 68,077 closed trades with a 100.0% win rate (closed only), hitting a max drawdown of -23.69%. [!] This 100.0% win rate is a structural artifact, not edge - closed-trade WR excludes open positions by definition, and 736 orders remain open carrying -2,540.32 USDT of unrealized loss. The losing trades are sitting unrealised in the open buy-ladder, not absent. Annualized growth (CAGR) was -22.02%.

Performance KPI Metrics

Data source - GET /backtests/{id} + /equity + /trades * Fields used: server aggregates from /daily-stats and /extras (Total Fees, Gross Profit/Loss, fill-type counts) plus <AnalyticsCharts> KPI strip * Transform: bt.* + dailyStats.totals + extras.deep_dive.fill_type_breakdown

Six-card KPI strip mirroring the live UI - combines risk (Max Drawdown, Sharpe), trade-economics (Profit Factor, Fees, Hold Time) and exit-mix (TP/SL/TSL) into one retrieval-friendly chunk.

Max Drawdown

-23.69%

Profit Factor

inf (no losing trades)

Sharpe Ratio

-1.58

Total Fees

516.44 USDT

Avg Hold Time

7.7 h

TP / SL / TSL

68,077 / 0 / 0

Trade Economics

Metric	Value	Interpretation
Profit Factor	inf (no losing trades)	Sum of winning trades / sum of losing trades. >1 = profitable edge, >2 = strong, inf = no losses.
Gross Profit	1,032.76 USDT	Sum of all winning trades before fees.
Gross Loss	0.00 USDT	Absolute sum of all losing trades.
Total Fees	516.44 USDT	Buy + sell fees across all trades.
Fee Drag	50.0% of gross profit	What share of edge the exchange consumed.
Avg Hold Time	7.7 h	Mean duration a position is open.

Exit Mix (Fill Types)

Exit Type	Count	Share	What It Means
TP (Take Profit)	68,077	100.0%	Position closed by hitting the profit target - the desired outcome.
SL (Stop Loss)	0	0.0%	Position closed by hitting the stop - capital preservation in action.
TSL (Trailing Stop)	0	0.0%	Trailing stop locked in profit during a trend.

Verdict

For AGLDUSDT, the strategy’s profit factor of inf (no losing trades) indicates gross winners materially exceeded gross losers in this window, exits are TP-dominant (100.0%), so most profit comes from hitting take-profit targets, fees ate 50.0% of gross profit - over-trading risk.

Chunking & Headings Guide

This document is structured for deterministic RAG ingestion. Each top-level section is wrapped in HTML-comment chunk markers and follows a stable heading convention.

Chunk markers

Every section is delimited by paired JSX comments - invisible to readers, trivial for splitters. (Earlier versions of this exporter used HTML comments; we switched to JSX comments in v2.4.0 because MDX 3 - the parser Mintlify uses - forbids HTML comments at the document level.) The example below uses uppercase RAG-CHUNK so it doesn’t accidentally match a splitter that scans this very page; real markers use lowercase rag-chunk:

{/* RAG-CHUNK:start id="tldr" type="summary" title="TL;DR" tokens~="120" backtest_id="14c346cf-9049-46a6-ac75-e17a2e31b31c" */}
...section markdown...
{/* RAG-CHUNK:end id="tldr" */}

Splitter rule: match the case-sensitive lowercase regex \{/\*\s*rag-chunk:(start|end)\b[^*]*\*/\} and treat everything between a matching start/end pair (same id) as one indivisible chunk. Never split inside a chunk. (For backwards compatibility with v2.3.x exports, splitters can OR-match the legacy regex <!--rag-chunk:(start|end)\b[^>]*-->.)

Heading convention

• # Title - appears exactly once (the document title in the intro chunk).
• ## Section - opens every top-level chunk. Heading text matches the chunk’s title attribute.
• ### Sub-section - purely visual; never introduces a new chunk.
• #### and deeper - reserved for inline detail (e.g. per-chart sub-blocks inside the catalogue).

Chunk type taxonomy

Type	Purpose	Best for retrieval of…
meta	Frontmatter & document intro	document-identity questions
summary	TL;DR-style condensed prose	”give me the headline”
kpis	Headline numbers, card grids	numeric lookups
overview	Tabular meta (period, balance, …)	“what was tested”
comparison	Strategy vs benchmark	”did it beat buy & hold”
claims	Assertion -> evidence -> source	citation-grade answers
config	Reproduction parameters	”how do I rerun this”
chart	Single visualization w/ embedded data	chart-specific questions
catalogue	Multi-chart bundle	exploratory chart questions
risk / allocation / trade-stats / monthly / top-trades	Domain-specific analytics	targeted KPI questions
validation	Data-quality report	trust / sanity questions
facts	Machine-readable JSON + key-value	deterministic lookups
qa	Pre-answered FAQ	direct Q&A retrieval
glossary	Term definitions	disambiguation
narrative	Human-oriented prose	”explain like I’m…”
structured	JSON-LD / schema.org	search-engine indexing
provenance	Metric -> API field -> formula lookup	”where does this number come from”
integrity	SHA-256 fingerprints of raw API payloads	tamper-evidence checks
attachments	Verbatim API responses + trade-sample CSV (full ledger via /trades pagination)	byte-level reproduction
footer	Disclaimers, links	compliance lookups

Recommended ingestion pipeline

1. Split on {/* rag-chunk:start ... */} / {/* rag-chunk:end ... */} boundaries.
2. Use the id attribute as the vector-store primary key - stable across re-exports.
3. Store type, title, tokens~, and backtest_id as filterable metadata.
4. Do not further chunk the facts, claims, qa, or structured sections - they’re designed to be retrieved whole.
5. The catalogue chunk is large; if your context budget is tight, split it on its ### sub-headings instead of dropping it.

Performance Analysis

Data source - GET /backtests/{id} + /equity + /trades * Fields used: same prose rendered on the live page by <BacktestInterpretation> - pure, deterministic, no LLM * Transform: buildBacktestInterpretation(bt)

Long-form, rule-based interpretation of this run. Identical to what the page shows - embedded here so an LLM can quote it verbatim without re-deriving the analysis.

This run produced a -21.95% return on AGLDUSDT - a clear loss in the tested window. Useful primarily as a negative datapoint about parameter combinations that did not fit AGLDUSDT market conditions over these dates.

About AGLDUSDT: AGLDUSDT is a stablecoin-quoted spot pair on Binance. Quote-side liquidity is deep, so slippage assumptions in this backtest map reasonably well to live execution at retail size.

An 100.0% closed-trade win rate across 68.077 closed trades on AGLDUSDT is unusually high. Strategies that win this often typically use small take-profits relative to stop-losses, which works until a single large adverse AGLDUSDT move erases many small wins. This figure covers closed trades only and excludes 736 orders that were still open at the end of the window.

At roughly 186.5 AGLDUSDT trades per day this is a high-frequency configuration - fee drag and slippage assumptions become critical when extrapolating to live trading on Binance Spot.

The trade payoff distribution is positively skewed - outsized winners drove the bulk of the result, which is characteristic of trend-capturing modes. Best single trade: 0.0175 USDT. Worst: 0.0050 USDT. Average per trade: 0.0076 USDT.

Risk profile (closed trades only): No closed trade ended in a loss in this window - the worst closed trade still finished at +0.00% of starting capital and the best at +0.00%, giving a best-vs-worst ratio of 3.50:1. This is a closed-trade statistic only: open positions and unrealized PnL are not reflected in the per-trade min/max, so this should not be read as “the strategy cannot lose”. Drawdown on the equity curve and any negative unrealized PnL on still-open positions remain the relevant downside measures.

About the LongTimeLong strategy: LongTimeLongMoreProfit holds positions longer to capture larger swings. It accepts deeper drawdowns in exchange for higher per-trade payoff.

Configuration analysis: The LongTimeLong configuration entered on a 0.1% pullback signal across 9 potential buy splits at 25 USDT each. Total deployable notional is therefore 225 USDT - a position-sizing footprint that is defensive at 2% of starting capital - most of the account stays in stablecoins as buffer. No hard stop-loss is configured - the strategy relies on take-profit zones and trailing logic instead, which trades smoother behaviour for higher tail-risk in sustained downtrends. Profit is taken in 9 laddered sell zones, which scales out gradually rather than betting on a single exit price - a structure that smooths returns at the cost of capping the very best winners. Maker/taker fees totalling 15 bps were deducted from every fill, so the headline -21.95% is already net of trading costs - no additional fee adjustment is required when comparing to other runs.

Over the configured 365-day window the strategy reported 516.31 USDT of realised trade profit on a 10000 USDT starting balance, ending at a portfolio value of 7805.07 USDT. Mechanically annualising the -21.95% window return projects to roughly -21.9% per year - the window covers roughly one full year, so the annualised figure is closer to the realised pace than to an extrapolation, but a single year still represents a single market regime. Treat this number as a unit-conversion of the window result, not as an expected forward return.

Methodology & data

This backtest was executed on historical Binance Spot 1-minute candles for AGLDUSDT, with intrabar fill simulation in “OLHC” mode and a synthetic order latency of 2s applied to each fill to approximate real-world routing delay. The simulator processes every minute sequentially, evaluates the LongTimeLong rule set, and books fills against the next available bar - a standard event-driven backtesting approach that avoids look-ahead bias. Equity is marked-to-market on every closed trade and aggregated into the equity curve shown above.

Configured backtest window: approximately 12.0 months (365 days from config.from to config.to) of AGLDUSDT 1-minute price action - a sample size that is large enough to span multiple short-term regimes. Note: the equity series may cover fewer days if the engine omits leading or trailing flat periods (e.g. dates before the asset began trading); see the Overview section for the exact equity-coverage span.

Live trading considerations

Translating this result to live trading: AGLDUSDT is a deeply-liquid USDT-quoted pair on Binance, so the simulated fills here translate well to live execution at retail size. The high trade frequency means cumulative slippage and exchange-side latency will erode a few percent of the headline return over a full year - budget for that gap. Without a hard stop-loss, the live system depends on the take-profit ladder firing during recovery legs; a prolonged downtrend without recovery will hold positions open longer than backtest aggregates suggest. Additionally, exchange downtime, API rate limits, and funding-rate changes (on perp variants) are not modelled here and should be accounted for in production deployment.

Frequently asked questions

Deterministic Q&A - every question is always present so this section can be sliced into stable RAG chunks. Missing API fields render as null; metrics that can’t be derived locally render as “not available”.

Is a -21.95% return on AGLDUSDT a good backtest result?

No - a negative return means the strategy lost capital and the configuration was unsuitable for this market regime.

What does the 100.0% win rate mean here?

It means 100.0 out of every 100 closed trades ended profitable. Frequent wins are emotionally easier to operate but say nothing about size - one large loss can offset many small wins.

What is the annualised return for this AGLDUSDT backtest?

Compounding the -21.95% over 365 days projects to -21.9% per year. This is a directional indicator only - crypto regimes change, and strategies rarely sustain peak performance year-over-year.

Can I run this exact LongTimeLong configuration live?

The configuration shown in the Strategy Configuration block is the same JSON schema the live unCoded TradingBot consumes, so it can be loaded into a live instance. That is a technical compatibility statement, not a recommendation: a passing backtest is necessary but not sufficient evidence that a configuration will be profitable in live trading. Before any live use, validate on an out-of-sample window, paper-trade it, confirm exchange-side fees match the simulated 7.5/7.5 bps, and start with a position size well below the backtested capital to absorb live slippage and execution differences.

How is this backtest different from others on AGLDUSDT?

Every run on the platform uses the same intrabar-fill engine and historical Binance Spot data, so the comparison is apples-to-apples. What differs between runs is the LongTimeLong parameter set (buy trigger, sell zones, splits, stop-loss) and the time window - both are visible above so you can rerun, tune, or fork this configuration.

This interpretation is generated deterministically from this run’s own metrics. Past performance is not indicative of future results - a profitable backtest is necessary but not sufficient evidence that a strategy will work in live trading on AGLDUSDT.

Overview

Data source - GET /backtests/{id} * Fields used: symbol, mode_name, config.from, config.to, start_balance, final_value, avg_profit, status, created_at, started_at, completed_at, elapsed_sec, is_duplicate, data_file, id, summary_text

Field	Value
Trading Pair	AGLDUSDT
Strategy	LongTimeLong
Period	2025-01-01 -> 2025-12-31
Configured window (inclusive elapsed)	365.0 days (1.00 years) - (config.to - config.from) / 86 400 000 ms
Configured window (calendar days)	365 days - Math.round of the elapsed value above; this is what the narrative paragraph quotes
Equity-stat duration	364.0 days (1.00 years) - first -> last equity sample timestamp; used for CAGR / Sharpe / Sortino
Start Balance	10,000.00 USDT
Final Value	7,805.07 USDT
Avg Profit / Trade	+0.0076 USDT
Trades / Month	5677.0
Status	completed
Created	2026-04-19 17:13:49 UTC
Started	2026-04-20 02:51:43 UTC
Completed	2026-04-20 03:04:20 UTC
Compute Time	9m 45s
Backtest ID	14c346cf-9049-46a6-ac75-e17a2e31b31c
Engine summary:

Backtest AGLDUSDT (Mode: 3LongTimeLong.json)
Period: 2025-01-01 00:00:01 to 2025-12-31 23:59:59
Starting balance: 10,000.00 USDT
Final value: 7,805.07 USDT
P&L: -2,194.93 USDT (-21.95%)
Result: LOSS
Completed trades: 68077
Open orders at end: 736
Win rate: 100.0%
Avg. profit/trade: 0.007584 USDT
Best trade: 0.017462 USDT
Worst trade: 0.004991 USDT
Total profit (trades only): 516.314012 USDT

Strategy parameters:
  Buy trigger: -0.1% from last buy
  Buy splits: 9
  Sell targets: [0.25, 0.35, 0.5, 0.75, 1.0, 2.5, 5.0, 10.0, 15.0]
  Investment per buy: 25.0 USDT
  Fees: maker 7.5 bps / taker 7.5 bps

Elapsed: 584.6s

Verifiable Claims

Each claim is grounded in a specific field + source so an LLM can cite evidence rather than paraphrase.

Claim	Evidence	Source
Strategy returned -21.95% over the tested window	return_pct = -21.9493	GET /backtests/{id} -> return_pct
Capital grew from 10,000.00 to 7,805.07 USDT	final_value - start_balance = -2,194.93 USDT	GET /backtests/{id} -> start_balance, final_value
Roughly 68077 of 68077 trades were profitable	win_rate = 100%	GET /backtests/{id} -> win_rate, fulfilled_trades
Worst peak-to-trough loss was 23.69%	max_drawdown_pct = 23.6869	derived client-side from /equity series
Risk-adjusted return (Sharpe, annualized, rf=0) = -1.58	sharpe_annualized = -1.5763	derived from daily-resampled equity returns

Strategy Configuration

Data source - GET /backtests/{id} * Fields used: mode_name, config (full JSON payload)

Mode LongTimeLong governs how buy triggers, position sizing and exits behave. Below are the exact parameters used for this run.

Highlights

Parameter	Value
canBuy	true
canSell	true
canBuyUp	true
startBal	10000
stepSize	0.1
stopLoss	false
tickSize	0.001
buySplits	9

Full JSON

{
  "to": "2025-12-31 23:59:59",
  "from": "2025-01-01 00:00:01",
  "canBuy": true,
  "symbol": "AGLDUSDT",
  "canSell": true,
  "canBuyUp": true,
  "startBal": 10000,
  "stepSize": 0.1,
  "stopLoss": false,
  "tickSize": 0.001,
  "buySplits": 9,
  "buyVolumes": [
    20,
    15,
    10,
    10,
    10,
    10,
    5,
    5,
    5
  ],
  "canBuyDown": false,
  "minNotional": 5,
  "buyPercentage": 0.1,
  "fees_in_quote": true,
  "intrabar_mode": "OLHC",
  "maker_fee_bps": 7.5,
  "taker_fee_bps": 7.5,
  "sellPercentages": [
    0.25,
    0.35,
    0.5,
    0.75,
    1,
    2.5,
    5,
    10,
    15
  ],
  "triggerCoolDown": 1,
  "investmentPerBuy": 25,
  "assumed_spread_bps": 0,
  "stopLossPercentage": 5,
  "investmentPercentMode": false,
  "minInvestmentPerQuote": 25,
  "order_latency_seconds": 2,
  "trailingStopLossPercentages": [
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0
  ],
  "sellCancelDistancePercentage": 1,
  "dontBuyBelowQuoteAssetBalance": 1,
  "investmentPerFreeQuotePercent": 0.01,
  "sellActivateDistancePercentage": 0.1
}

Full Mode Configuration

Every parameter the strategy received at runtime, validated against the canonical engine schema (ENGINE.md Section 20). Recognised parameters are passed verbatim into POST /backtests to reproduce the run.

Engine Parameters

Parameter	Value	Meaning
assumed_spread_bps	0	Spread (basis points) added to taker fills on top of the candle price.
buyPercentage	0.1	Buy-trigger threshold as a fraction (e.g. 0.1 = -0.1% from last reference price).
buySplits	9	Number of price levels in the buy-ladder (DCA depth).
buyVolumes	[20,15,10,10,10,10,5,5,5]	Per-split sizing weights (sum ~ 100). Distributes investmentPerBuy x buySplits across the ladder.
canBuy	true	Master switch - disables all buy-side logic when false.
canBuyDown	false	Allow re-entries below the last buy price.
canBuyUp	true	Allow re-entries above the last buy price (true) or only below (false).
canSell	true	Master switch - disables all sell-side logic when false.
dontBuyBelowQuoteAssetBalance	1	Refuse to open new buys if free quote drops below this absolute USDT amount.
fees_in_quote	true	If true, fees are deducted from the quote (USDT) leg of every fill.
intrabar_mode	OLHC	Intra-bar fill model (OLHC, OHLC, …) - see ENGINE.md Section 4.
investmentPerBuy	25	Notional invested per buy event, in quote (USDT).
investmentPerFreeQuotePercent	0.01	When investmentPercentMode = true: fraction of free quote balance to commit per buy.
investmentPercentMode	false	If true, sizing is taken as a fraction of free quote balance instead of fixed USDT.
maker_fee_bps	7.5	Maker fee in basis points (e.g. 7.5 = 0.075%).
minInvestmentPerQuote	25	Hard floor on per-buy notional, regardless of percent-mode math.
minNotional	5	Exchange minimum notional per order (USDT).
order_latency_seconds	2	Latency injected between signal time and order placement.
sellActivateDistancePercentage	0.1	Distance from buy price (%) at which a TSL becomes armed.
sellCancelDistancePercentage	1	Distance (%) at which an unfilled sell order is cancelled and re-priced.
sellPercentages	[0.25,0.35,0.5,0.75,1,2.5,5,10,15]	Take-profit ladder, each value is the per-tier profit target as a fraction.
startBal	10000	Initial portfolio value in the quote currency (USDT).
stepSize	0.1	Exchange size step (base asset).
stopLoss	false	Hard stop-loss enabled flag.
stopLossPercentage	5	Hard stop-loss distance from entry as a percent (5.0 = -5%).
taker_fee_bps	7.5	Taker fee in basis points.
tickSize	0.001	Exchange price tick (quote currency).
trailingStopLossPercentages	[0,0,0,0,0,0,0,0,0]	Per-tier trailing-SL distances. Zero entries disable TSL on that tier.
triggerCoolDown	1	Minimum bars/ticks between two consecutive buys on the same level.

YAML

to: "2025-12-31 23:59:59"
from: "2025-01-01 00:00:01"
canBuy: true
symbol: AGLDUSDT
canSell: true
canBuyUp: true
startBal: 10000
stepSize: 0.1
stopLoss: false
tickSize: 0.001
buySplits: 9
buyVolumes:
  - 20
  - 15
  - 10
  - 10
  - 10
  - 10
  - 5
  - 5
  - 5
canBuyDown: false
minNotional: 5
buyPercentage: 0.1
fees_in_quote: true
intrabar_mode: OLHC
maker_fee_bps: 7.5
taker_fee_bps: 7.5
sellPercentages:
  - 0.25
  - 0.35
  - 0.5
  - 0.75
  - 1
  - 2.5
  - 5
  - 10
  - 15
triggerCoolDown: 1
investmentPerBuy: 25
assumed_spread_bps: 0
stopLossPercentage: 5
investmentPercentMode: false
minInvestmentPerQuote: 25
order_latency_seconds: 2
trailingStopLossPercentages:
  - 0
  - 0
  - 0
  - 0
  - 0
  - 0
  - 0
  - 0
  - 0
sellCancelDistancePercentage: 1
dontBuyBelowQuoteAssetBalance: 1
investmentPerFreeQuotePercent: 0.01
sellActivateDistancePercentage: 0.1

Strategy Logic

This section explains what the strategy actually does - the rules behind the parameters in Strategy Configuration. Without it the report would be a black box: numbers without semantics. We treat its absence as a defect, not a feature.

Black-box notice: this export does not include a machine-readable description of the strategy rules. The numeric results are still fully reproducible from the parameters in Strategy Configuration + the raw API attachments below, but the semantics of those parameters live in the engine source. Pass a strategyLogic object to buildVerifiableMdx (or wire up getModes() so the exporter can derive a sketch) to remove this notice.

Source unavailable in this export. Fetch GET /modes and select the entry whose filename == "3LongTimeLong.json" to inspect the public rule fields, or consult the engine source for the executable logic.

Engine Specification

The execution engine is shared across every backtest on this site. Its static semantics - TP-vs-SL ordering inside an OLHC bar, TSL splits bypassing nominal TP, JS-legacy boolean parsing, Binance hard caps, fee model, equity sampling - are documented once in the canonical Engine Specification and pinned per-run via the engine_version field in this document’s frontmatter (see also the Data Source Metadata section).

Engine Specification

Canonical, versioned spec covering the bar-loop, order types, intra-bar fill rules, gap handling, fees, position sizing, risk management, multi-entry cascade, sell-time curves, portfolio accounting, determinism guarantees and the full configuration schema (Section 20).

Why this is a link, not an inline copy: the spec is identical for every backtest - embedding ~700 lines into each export would bloat every report 5-10x without adding per-run information. The single source of truth lives at uncoded.ch/docs/engine; this report only pins which revision applied (engine_version in the frontmatter).

Data Source Metadata

Inputs the engine consumed for this run. Anything marked not provided is explicitly missing rather than silently omitted - pass the corresponding field via buildVerifiableMdx({ dataSource: ... }) to fill it in. The engine spec describes deterministic execution semantics (segment order, fill rules, fee classification) that cannot be reverse-engineered from trades alone.

Field	Value	Source
Market-data provider	Binance Spot	caller DataSourceInput.provider
Exchange / venue	binance	caller DataSourceInput.exchange
Symbol	AGLDUSDT	GET /backtests/{id} -> $.symbol
Candle interval	1m	platform invariant - all backtests run on 1-minute OHLCV candles
First candle (UTC)	2025-01-01 00:00:01	GET /backtests/{id} -> $.config.from
Last candle (UTC)	2025-12-31 23:59:59	GET /backtests/{id} -> $.config.to
Timezone	UTC	default assumption (UTC)
Engine version	uncoded-engine (FastAPI backend)	caller DataSourceInput.engineVersion
Engine spec version	1.1	caller DataSourceInput.engineSpecVersion
Engine spec document	https://uncoded.ch/docs/engine	caller DataSourceInput.engineSpecPath

Full data-source coverage (10/10 fields populated).

Equity Curve

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net, total_value_mid, total_value

Portfolio value evolution from 10,000.00 USDT to 7,805.07 USDT across 365 data points.

    10,006 |###                                                         
           |:::##   ##                                                  
           |:::::###::#         ####                                    
           |:::::::::::####   ##::::###    #### #                       
           |:::::::::::::::###:::::::::####::::#:#######                
           |::::::::::::::::::::::::::::::::::::::::::::##              
           |::::::::::::::::::::::::::::::::::::::::::::::#             
           |:::::::::::::::::::::::::::::::::::::::::::::::###          
           |::::::::::::::::::::::::::::::::::::::::::::::::::#######   
     7,757 |:::::::::::::::::::::::::::::::::::::::::::::::::::::::::###
           +------------------------------------------------------------
           start                                                   end

Risk & Quality Metrics

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net, total_value_mid, total_value, base_asset_bal (for time-in-market)

Drawdown, CAGR, Sharpe, Sortino & Volatility are computed client-side from the equity series - not returned by the API directly.

Derived from 365 equity samples across 364.0 days. Sharpe & Sortino are annualized using daily-resampled returns (rf = 0).

Max Drawdown

-23.69% (-2,370.81 USDT)

CAGR

-22.02%

Volatility (ann.)

15.05%

Sharpe Ratio

-1.58

Sortino Ratio

-1.45

Time in Market

99.7%

Drawdown Detail

Field	Value
Peak	10,008.93 USDT @ 2025-01-03 23:59:00 UTC
Trough	7,638.12 USDT @ 2025-12-18 23:59:00 UTC
Drawdown Duration	349.0 days
Recovery	not yet recovered within window
All-time Peak	10,008.93 USDT

Final Portfolio Allocation

Data source - GET /backtests/{id}/equity * Fields used: total_value_exit_net, quote_asset_bal, base_asset_bal, base_value_exit_net, base_value_mid, unrealized_pnl_exit_net

Snapshot at backtest end (2026-04-20 03:04:20 UTC):

Asset	Quantity	Value (USDT)	Allocation
USDT (Cash)	6,616.85	6,616.85	84.8%
AGLD	4,538.60000000	0.00	0.0%
Other (open positions, fee reserves, …)	-	1,188.22	15.2%
Total	-	7,805.07	100.0%

Unrealized P&L on remaining position: -2,540.32 USDT across 736 open order(s). The “Other” row, when present, is dominated by these open positions held outside the cash/base buckets.

Trade Analytics

Data source - GET /backtests/{id}/trades * Fields used: profit, profit_percentage, fill_type, buy_time, sell_time, buy_price, sell_price, buy_quantity, buy_fee_in_quote, sell_fee_in_quote

Profit Factor, Payoff, Expectancy, streaks, holding stats and volume are aggregated client-side from the per-trade rows above.

Wins

100 (100.0%)

Losses

0 (0.0%)

Breakeven

0

Profit Factor

inf

Payoff Ratio

inf

Expectancy / Trade

+0.0101 USDT

Avg Win

+0.0101 USDT

Avg Loss

-0.0000 USDT

Median Trade

+0.0093 USDT

Max Win Streak

100

Max Loss Streak

0

Avg Holding

3.6h

Median Holding

0.1h

Total Volume

1,006 USDT

Total Fees

516.44 USDT _{* engine-tracked, includes open-position buy fees}

Gross Performance

Metric	Value	Source
Gross Profit (winners only)	+1.01 USDT	Sum profit where profit > 0 (client-side from /trades)
Gross Loss (losers only)	-0.00 USDT	Sum -profit where profit < 0 (client-side from /trades)
Net Profit	+1.01 USDT	Gross Profit - Gross Loss
Closed-trade buy fees	0.38 USDT	Sum buy_fee_in_quote over matched-pair rows of /trades
Closed-trade sell fees	0.38 USDT	Sum sell_fee_in_quote over matched-pair rows of /trades
Closed-trade fees subtotal	0.75 USDT	buy + sell on matched pairs
Open-position buy fees	515.69 USDT	bt.total_fees - closed-trade subtotal - buy-side fees on the 736 orders still open at end of window (no matching sell row yet)
Total Fees Paid (authoritative)	516.44 USDT	bt.total_fees - engine-tracked sum of every fee actually paid (closed + open buy fills). reconciles to closed-fees + open-position buy fees
Fees as % of Gross Profit	51050.83%	Total Fees / Gross Profit

Fee accounting reconciles by construction. bt.total_fees is the engine-tracked total of every fee actually paid. Per-trade rows in /trades only emit on matched buy+sell pairs, so positions still open at end-of-window contribute their buy-fee to bt.total_fees but not to the per-trade sum. The identity is exact: bt.total_fees = Sum closed-trade (buy_fee + sell_fee) + Sum open-position buy_fees Citation rules: use Total Fees Paid for the headline (the authoritative number every consumer should quote), use Closed-trade fees subtotal when reasoning specifically about realized round-trip economics, and use Open-position buy fees when sizing the unrealized exposure of the still-open ladder.

Advanced - mechanical breakdown from API field mapping

Every figure above is derivable from two API surfaces. This table maps each line item to its exact source field so an external reviewer can re-compute the totals from the raw responses without trusting any client-side aggregation.

Line item	Formula	API field(s)	Endpoint
Closed-trade buy fees	Sum row.buy_fee_in_quote	buy_fee_in_quote per row	GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c/trades
Closed-trade sell fees	Sum row.sell_fee_in_quote	sell_fee_in_quote per row	GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c/trades
(A) Closed-trade subtotal	Sum (buy_fee_in_quote + sell_fee_in_quote)	both fee fields, matched-pair rows only	GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c/trades
(B) Total Fees Paid (engine)	direct read	total_fees (scalar)	GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c
(C) = (B) - (A) Open-position buy fees	bt.total_fees - closed_subtotal	derived	-
Open orders count	direct read	active_orders	GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c
Numerical walk-through for this run:

(A) Closed-trade subtotal     = 0.376623  +  0.377948
                              = 0.754571 USDT
(B) bt.total_fees             = 516.444790 USDT     <- authoritative
(C) Open-position buy fees    = (B) - (A)
                              = 516.444790 - 0.754571
                              = 515.690218 USDT
Identity check:  (A) + (C) ?= (B)
                 0.754571 + 515.690218 = 516.444790 USDT
                 vs bt.total_fees = 516.444790 USDT  ->   matches within 0.01

Why (A) != (B) in general: /trades rows emit only on matched buy+sell pairs. A buy fill that hasn’t found its sell yet (still sitting in the DCA ladder at end-of-window) has already paid its fee - that fee is included in bt.total_fees (B) but not in any per-trade row, so it is not part of (A). The remainder (C) is therefore the buy-side fee paid on the 736 open orders.

Holding Period Distribution

Min	Median	Avg	Max
0.0h	0.1h	3.6h	235.1h

Trade Timeline

First Trade	Last Trade
2025-01-01 00:01:00 UTC	2025-12-31 23:58:00 UTC

Breakdown by Exit Type

Type	Count	Cumulative Profit	Avg Return
TP	100	+1.01 USDT	+0.20%

Monthly Performance

Data source - GET /backtests/{id}/trades * Fields used: sell_time (bucket key), profit

Realized P&L bucketed by sell-time month (4 active months).

Best month: 2025-10 with +0.52 USDT

Month	Trades	Wins	Win Rate	Net P&L (USDT)
2025-01	30	30	100.0%	+0.19
2025-10	44	44	100.0%	+0.52
2025-11	1	1	100.0%	+0.02
2025-12	25	25	100.0%	+0.29

Top Trades

Data source - GET /backtests/{id}/trades * Fields used: fill_type, buy_time, sell_time, buy_price, sell_price, profit, profit_percentage

Top 10 Winners

Showing top 10 of 100 profitable trades.

#	Type	Period	Buy	Sell	Profit	%
1	TP	2025-10-11 -> 2025-10-11	0.401000	0.403000	+0.0175	+0.35%
2	TP	2025-10-11 -> 2025-10-11	0.401000	0.403000	+0.0175	+0.35%
3	TP	2025-10-11 -> 2025-10-11	0.401000	0.403000	+0.0175	+0.35%
4	TP	2025-10-11 -> 2025-10-12	0.401000	0.403000	+0.0175	+0.35%
5	TP	2025-10-12 -> 2025-10-12	0.401000	0.403000	+0.0175	+0.35%
6	TP	2025-10-17 -> 2025-10-17	0.401000	0.403000	+0.0175	+0.35%
7	TP	2025-10-17 -> 2025-10-17	0.401000	0.403000	+0.0175	+0.35%
8	TP	2025-10-17 -> 2025-10-18	0.401000	0.403000	+0.0175	+0.35%
9	TP	2025-10-18 -> 2025-10-18	0.401000	0.403000	+0.0175	+0.35%
10	TP	2025-10-19 -> 2025-10-19	0.401000	0.403000	+0.0175	+0.35%

Top 10 Losers

No losing trades in this run (win rate = 100%).

Charts Catalogue

Data source - GET /backtests/{id} + /equity + /trades * Fields used: every chart from the live <AnalyticsCharts> UI, with the exact transform + API fields it consumes

Below is the complete set of analytical charts shown in the backtester UI, regrouped into the same five tabs. Each card lists what it shows, how to read it, why it matters, the API fields + transform that produce it, and a down-sampled JSON preview of the underlying series so you can re-render the visualisation in any tool (Notion, Observable, a notebook, …) without calling the API again.

JSON previews are intentionally down-sampled (<= 60 points for line/area series, full bins for histograms). For the full series, hit the corresponding REST endpoint with your X-API-Key.

Equity & Balance

Drawdown

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net * Transform: computeDrawdown(equity)

What it shows. Percentage decline of the portfolio from its running all-time peak. Always <= 0%.

How to read it. Each point is the relative drop from the highest equity value seen so far. The deepest valley is the maximum drawdown - the worst peak-to-trough loss any investor would have lived through.

Why it matters. The single most important risk metric. A high return with a 60% drawdown is psychologically unsurvivable for most live traders.

Insights (auto-derived from drawdown).

Stat	Value
Shallowest DD	0.00% @ 2025-01-01 23:59:00 UTC
Deepest DD	-23.69% @ 2025-12-18 23:59:00 UTC
Mean	-10.20%
Std Dev	6.00%
Last	-22.02%
Points	365
Across 365 points the series ranged from -23.69% (2025-12-18 23:59:00 UTC) to 0.00% (2025-01-01 23:59:00 UTC), averaging -10.20%. 0% of points were positive, 99% negative. The most recent value is -22.02%.
Showing 60 of 365 points (down-sampled for readability).

Drawdown

[{"timestamp":"2025-01-01T23:59:00+00:00","drawdown":0},{"timestamp":"2025-01-07T23:59:00+00:00","drawdown":-0.3318898498844762},{"timestamp":"2025-01-13T23:59:00+00:00","drawdown":-1.4634601538644387},{"timestamp":"2025-01-20T23:59:00+00:00","drawdown":-2.842002058069736},{"timestamp":"2025-01-26T23:59:00+00:00","drawdown":-2.78170312115122},{"timestamp":"2025-02-01T23:59:00+00:00","drawdown":-4.488689460060413},{"timestamp":"2025-02-07T23:59:00+00:00","drawdown":-6.396253046473759},{"timestamp":"2025-02-13T23:59:00+00:00","drawdown":-5.437127702658583},{"timestamp":"2025-02-19T23:59:00+00:00","drawdown":-3.7972726222764575},{"timestamp":"2025-02-26T23:59:00+00:00","drawdown":-1.9300608676689108},{"timestamp":"2025-03-04T23:59:00+00:00","drawdown":-4.0685432659968646},{"timestamp":"2025-03-10T23:59:00+00:00","drawdown":-7.007070355399842},{"timestamp":"2025-03-16T23:59:00+00:00","drawdown":-7.803571313094286},{"timestamp":"2025-03-22T23:59:00+00:00","drawdown":-6.458999320871982},{"timestamp":"2025-03-28T23:59:00+00:00","drawdown":-7.24165146106939},{"timestamp":"2025-04-04T23:59:00+00:00","drawdown":-9.23054208566014},{"timestamp":"2025-04-10T23:59:00+00:00","drawdown":-10.955956069086895},{"timestamp":"2025-04-16T23:59:00+00:00","drawdown":-10.239994280652581},{"timestamp":"2025-04-22T23:59:00+00:00","drawdown":-7.536980939551762},{"timestamp":"2025-04-28T23:59:00+00:00","drawdown":-7.54534761995014},{"timestamp":"2025-05-04T23:59:00+00:00","drawdown":-5.666154607332871},{"timestamp":"2025-05-11T23:59:00+00:00","drawdown":-4.1794046579439925},{"timestamp":"2025-05-17T23:59:00+00:00","drawdown":-6.076999595796606},{"timestamp":"2025-05-23T23:59:00+00:00","drawdown":-6.652629205001008},{"timestamp":"2025-05-29T23:59:00+00:00","drawdown":-7.1809138536438635},{"timestamp":"2025-06-04T23:59:00+00:00","drawdown":-6.934619648242242},{"timestamp":"2025-06-10T23:59:00+00:00","drawdown":-6.656365070802741},{"timestamp":"2025-06-17T23:59:00+00:00","drawdown":-9.783456149773674},{"timestamp":"2025-06-23T23:59:00+00:00","drawdown":-9.7407823732581},{"timestamp":"2025-06-29T23:59:00+00:00","drawdown":-10.022091576858099},{"timestamp":"2025-07-05T23:59:00+00:00","drawdown":-9.833985806841484},{"timestamp":"2025-07-11T23:59:00+00:00","drawdown":-8.397316975074537},{"timestamp":"2025-07-17T23:59:00+00:00","drawdown":-7.907859812758589},{"timestamp":"2025-07-24T23:59:00+00:00","drawdown":-8.070773379135925},{"timestamp":"2025-07-30T23:59:00+00:00","drawdown":-9.099636693884088},{"timestamp":"2025-08-05T23:59:00+00:00","drawdown":-9.915120433846669},{"timestamp":"2025-08-11T23:59:00+00:00","drawdown":-9.443875920542306},{"timestamp":"2025-08-17T23:59:00+00:00","drawdown":-9.24370372257333},{"timestamp":"2025-08-23T23:59:00+00:00","drawdown":-8.95016992651185},{"timestamp":"2025-08-30T23:59:00+00:00","drawdown":-9.156778370292502},{"timestamp":"2025-09-05T23:59:00+00:00","drawdown":-10.114470833446013},{"timestamp":"2025-09-11T23:59:00+00:00","drawdown":-9.61888978812055},{"timestamp":"2025-09-17T23:59:00+00:00","drawdown":-9.672471203660745},{"timestamp":"2025-09-23T23:59:00+00:00","drawdown":-12.245203229420536},{"timestamp":"2025-09-29T23:59:00+00:00","drawdown":-12.798234170659617},{"timestamp":"2025-10-06T23:59:00+00:00","drawdown":-11.883226674028801},{"timestamp":"2025-10-12T23:59:00+00:00","drawdown":-16.196044611159323},{"timestamp":"2025-10-18T23:59:00+00:00","drawdown":-17.184736711805666},{"timestamp":"2025-10-24T23:59:00+00:00","drawdown":-16.75165954234786},{"timestamp":"2025-10-30T23:59:00+00:00","drawdown":-18.63051223087992},{"timestamp":"2025-11-05T23:59:00+00:00","drawdown":-20.021820334878182},{"timestamp":"2025-11-12T23:59:00+00:00","drawdown":-19.190254544987052},{"timestamp":"2025-11-18T23:59:00+00:00","drawdown":-20.125240478749514},{"timestamp":"2025-11-24T23:59:00+00:00","drawdown":-20.75785222633479},{"timestamp":"2025-11-30T23:59:00+00:00","drawdown":-21.136164417946752},{"timestamp":"2025-12-06T23:59:00+00:00","drawdown":-20.99551516761728},{"timestamp":"2025-12-12T23:59:00+00:00","drawdown":-21.12398072596338},{"timestamp":"2025-12-19T23:59:00+00:00","drawdown":-22.84142417522576},{"timestamp":"2025-12-25T23:59:00+00:00","drawdown":-22.289657262435554},{"timestamp":"2025-12-31T23:59:00+00:00","drawdown":-22.01895985155346}]

Unrealized PnL

Data source - GET /backtests/{id}/equity * Fields used: timestamp, unrealized_pnl_exit_net * Transform: equity.map(p => ({ pnl: p.unrealized_pnl_exit_net }))

What it shows. Mark-to-market profit/loss of currently open positions, sampled along the backtest timeline.

How to read it. Above zero => open positions are in profit; below zero => positions are underwater. Spikes show how much paper-PnL the strategy carries before realising it.

Why it matters. Reveals the gap between realised (closed-trade) profit and the rollercoaster the strategy actually rides. Large negative excursions indicate the bot held losers.

Insights (auto-derived from pnl).

Stat	Value
Best Unrealized	0.0000 USDT @ 2025-01-01 23:59:00 UTC
Worst Unrealized	-2,692.74 USDT @ 2025-12-18 23:59:00 UTC
Mean	-1,190.33 USDT
Std Dev	697.43 USDT
Last	-2,540.32 USDT
Points	365
Sum	-434,471.89 USDT
Across 365 points the series ranged from -2,692.74 USDT (2025-12-18 23:59:00 UTC) to 0.0000 USDT (2025-01-01 23:59:00 UTC), averaging -1,190.33 USDT. 0% of points were positive, 100% negative. The most recent value is -2,540.32 USDT.
Showing 60 of 365 points (down-sampled for readability).

Unrealized PnL

[{"timestamp":"2025-01-01T23:59:00+00:00","pnl":0},{"timestamp":"2025-01-07T23:59:00+00:00","pnl":-42.447688525},{"timestamp":"2025-01-13T23:59:00+00:00","pnl":-170.30310615},{"timestamp":"2025-01-20T23:59:00+00:00","pnl":-324.2427157},{"timestamp":"2025-01-26T23:59:00+00:00","pnl":-329.5357811},{"timestamp":"2025-02-01T23:59:00+00:00","pnl":-511.65527845},{"timestamp":"2025-02-07T23:59:00+00:00","pnl":-681.30754185},{"timestamp":"2025-02-13T23:59:00+00:00","pnl":-538.0866739501},{"timestamp":"2025-02-19T23:59:00+00:00","pnl":-361.4247780251},{"timestamp":"2025-02-26T23:59:00+00:00","pnl":-190.9526838501},{"timestamp":"2025-03-04T23:59:00+00:00","pnl":-403.7765908751},{"timestamp":"2025-03-10T23:59:00+00:00","pnl":-699.8305354251},{"timestamp":"2025-03-16T23:59:00+00:00","pnl":-790.0086852501},{"timestamp":"2025-03-22T23:59:00+00:00","pnl":-667.6693808001},{"timestamp":"2025-03-28T23:59:00+00:00","pnl":-751.4698381501},{"timestamp":"2025-04-04T23:59:00+00:00","pnl":-960.5200285251},{"timestamp":"2025-04-10T23:59:00+00:00","pnl":-1141.4833632751},{"timestamp":"2025-04-16T23:59:00+00:00","pnl":-1078.5524735001},{"timestamp":"2025-04-22T23:59:00+00:00","pnl":-822.8549442501},{"timestamp":"2025-04-28T23:59:00+00:00","pnl":-836.6288861001},{"timestamp":"2025-05-04T23:59:00+00:00","pnl":-664.9437811251},{"timestamp":"2025-05-11T23:59:00+00:00","pnl":-530.8620631501},{"timestamp":"2025-05-17T23:59:00+00:00","pnl":-732.9151199501},{"timestamp":"2025-05-23T23:59:00+00:00","pnl":-801.5406319001},{"timestamp":"2025-05-29T23:59:00+00:00","pnl":-864.5510700751},{"timestamp":"2025-06-04T23:59:00+00:00","pnl":-850.6200822501},{"timestamp":"2025-06-10T23:59:00+00:00","pnl":-833.2497594501},{"timestamp":"2025-06-17T23:59:00+00:00","pnl":-1151.8775837501},{"timestamp":"2025-06-23T23:59:00+00:00","pnl":-1154.1808645751},{"timestamp":"2025-06-29T23:59:00+00:00","pnl":-1187.8784121251},{"timestamp":"2025-07-05T23:59:00+00:00","pnl":-1173.3314355251},{"timestamp":"2025-07-11T23:59:00+00:00","pnl":-1033.1839421751},{"timestamp":"2025-07-17T23:59:00+00:00","pnl":-995.7955685501},{"timestamp":"2025-07-24T23:59:00+00:00","pnl":-1028.1445144501},{"timestamp":"2025-07-30T23:59:00+00:00","pnl":-1139.2228870251},{"timestamp":"2025-08-05T23:59:00+00:00","pnl":-1227.6976405501},{"timestamp":"2025-08-11T23:59:00+00:00","pnl":-1185.0443357501},{"timestamp":"2025-08-17T23:59:00+00:00","pnl":-1168.7263259001},{"timestamp":"2025-08-23T23:59:00+00:00","pnl":-1143.7804987251},{"timestamp":"2025-08-30T23:59:00+00:00","pnl":-1171.5030522751},{"timestamp":"2025-09-05T23:59:00+00:00","pnl":-1271.9560685751},{"timestamp":"2025-09-11T23:59:00+00:00","pnl":-1226.1986460251},{"timestamp":"2025-09-17T23:59:00+00:00","pnl":-1235.2184608001},{"timestamp":"2025-09-23T23:59:00+00:00","pnl":-1495.7573125501},{"timestamp":"2025-09-29T23:59:00+00:00","pnl":-1549.8648995251},{"timestamp":"2025-10-06T23:59:00+00:00","pnl":-1458.4706156251},{"timestamp":"2025-10-12T23:59:00+00:00","pnl":-1891.3255379751},{"timestamp":"2025-10-18T23:59:00+00:00","pnl":-1999.7901508751},{"timestamp":"2025-10-24T23:59:00+00:00","pnl":-1962.8947145501},{"timestamp":"2025-10-30T23:59:00+00:00","pnl":-2155.941248725},{"timestamp":"2025-11-05T23:59:00+00:00","pnl":-2300.447334875},{"timestamp":"2025-11-12T23:59:00+00:00","pnl":-2224.56613825},{"timestamp":"2025-11-18T23:59:00+00:00","pnl":-2324.1015391251},{"timestamp":"2025-11-24T23:59:00+00:00","pnl":-2390.2783633251},{"timestamp":"2025-11-30T23:59:00+00:00","pnl":-2428.8083881251},{"timestamp":"2025-12-06T23:59:00+00:00","pnl":-2417.5527284251},{"timestamp":"2025-12-12T23:59:00+00:00","pnl":-2431.1439072751},{"timestamp":"2025-12-19T23:59:00+00:00","pnl":-2608.7531188001},{"timestamp":"2025-12-25T23:59:00+00:00","pnl":-2560.3587471251},{"timestamp":"2025-12-31T23:59:00+00:00","pnl":-2540.3158420751}]

Balance Composition (Cash vs. Coin)

Data source - GET /backtests/{id}/equity * Fields used: quote_asset_bal, base_value_exit_net, total_value_exit_net * Transform: stack(quote, base, total)

What it shows. Stacked area showing how the portfolio value splits between quote (cash) and base (coin held).

How to read it. Cash floor stacks first, coin value on top - the top of the stack equals total equity. Wider coin bands => heavier in-market exposure.

Why it matters. Lets you see whether outperformance came from holding coin during a rally or from disciplined cash management.

Insights (auto-derived from total).

Stat	Value
Peak Total Equity	10,008.93 USDT @ 2025-01-03 23:59:00 UTC
Trough Total Equity	7,638.12 USDT @ 2025-12-18 23:59:00 UTC
Mean	8,987.96 USDT
Std Dev	600.33 USDT
Last	7,805.07 USDT
Points	365
Sum	3,280,603.58 USDT
Across 365 points the series ranged from 7,638.12 USDT (2025-12-18 23:59:00 UTC) to 10,008.93 USDT (2025-01-03 23:59:00 UTC), averaging 8,987.96 USDT. 100% of points were positive, 0% negative. The most recent value is 7,805.07 USDT.
Showing 60 of 365 points (down-sampled for readability).

Balance Composition (Cash vs. Coin)

[{"timestamp":"2025-01-01T23:59:00+00:00","quote":10002.7783156,"base":0,"total":10002.7783156},{"timestamp":"2025-01-07T23:59:00+00:00","quote":9545.3273947251,"base":430.38416959999995,"total":9975.7115643251},{"timestamp":"2025-01-13T23:59:00+00:00","quote":9110.937542575,"base":751.5159399999993,"total":9862.453482575},{"timestamp":"2025-01-20T23:59:00+00:00","quote":8715.3328113749,"base":1009.1433744000005,"total":9724.4761857749},{"timestamp":"2025-01-26T23:59:00+00:00","quote":8737.169030275,"base":993.3424340000001,"total":9730.511464275},{"timestamp":"2025-02-01T23:59:00+00:00","quote":8398.84465855,"base":1160.81573475,"total":9559.6603933},{"timestamp":"2025-02-07T23:59:00+00:00","quote":8078.90498325,"base":1289.8287023999,"total":9368.7336856499},{"timestamp":"2025-02-13T23:59:00+00:00","quote":8224.32497515,"base":1240.4068965748993,"total":9464.7318717249},{"timestamp":"2025-02-19T23:59:00+00:00","quote":8328.0561598999,"base":1300.807661999901,"total":9628.8638218998},{"timestamp":"2025-02-26T23:59:00+00:00","quote":8648.7012876751,"base":1167.0504551999002,"total":9815.751742875},{"timestamp":"2025-03-04T23:59:00+00:00","quote":8302.3230061502,"base":1299.3895263999002,"total":9601.7125325501},{"timestamp":"2025-03-10T23:59:00+00:00","quote":8005.8942177752,"base":1301.7031898499008,"total":9307.5974076251},{"timestamp":"2025-03-16T23:59:00+00:00","quote":7829.4617808252,"base":1398.4144019999012,"total":9227.8761828251},{"timestamp":"2025-03-22T23:59:00+00:00","quote":8089.2514730502,"base":1273.2019817997998,"total":9362.45345485},{"timestamp":"2025-03-28T23:59:00+00:00","quote":7917.6803462251,"base":1366.4380023000003,"total":9284.1183485251},{"timestamp":"2025-04-04T23:59:00+00:00","quote":7569.4002661501,"base":1515.6514082499998,"total":9085.0516744001},{"timestamp":"2025-04-10T23:59:00+00:00","quote":7259.9622190002,"base":1652.3939743499004,"total":8912.3561933501},{"timestamp":"2025-04-16T23:59:00+00:00","quote":7329.2403224252,"base":1654.7759864998998,"total":8984.0163089251},{"timestamp":"2025-04-22T23:59:00+00:00","quote":7828.6494620502,"base":1425.909565149901,"total":9254.5590272001},{"timestamp":"2025-04-28T23:59:00+00:00","quote":7750.8659997001,"base":1502.8556122998998,"total":9253.721612},{"timestamp":"2025-05-04T23:59:00+00:00","quote":7928.5115534751,"base":1513.2971752498997,"total":9441.808728725},{"timestamp":"2025-05-11T23:59:00+00:00","quote":8054.6073647251,"base":1536.0091284998998,"total":9590.616493225},{"timestamp":"2025-05-17T23:59:00+00:00","quote":7803.4580618751,"base":1597.2294787749006,"total":9400.68754065},{"timestamp":"2025-05-23T23:59:00+00:00","quote":7693.7894661251,"base":1649.2837087999005,"total":9343.073174925},{"timestamp":"2025-05-29T23:59:00+00:00","quote":7612.9699980501,"base":1677.2275351999006,"total":9290.19753325},{"timestamp":"2025-06-04T23:59:00+00:00","quote":7694.3196575251,"base":1620.5292907998992,"total":9314.848948325},{"timestamp":"2025-06-10T23:59:00+00:00","quote":7851.3064388751,"base":1491.3928158498993,"total":9342.699254725},{"timestamp":"2025-06-17T23:59:00+00:00","quote":7397.896471125,"base":1631.8144206000006,"total":9029.710891725},{"timestamp":"2025-06-23T23:59:00+00:00","quote":7434.7978686,"base":1599.1842116249,"total":9033.9820802249},{"timestamp":"2025-06-29T23:59:00+00:00","quote":7420.046265925,"base":1585.779772500001,"total":9005.826038425},{"timestamp":"2025-07-05T23:59:00+00:00","quote":7434.390006375,"base":1590.2634072499995,"total":9024.653413625},{"timestamp":"2025-07-11T23:59:00+00:00","quote":7569.292861025,"base":1599.1557329999996,"total":9168.448594025},{"timestamp":"2025-07-17T23:59:00+00:00","quote":7666.717832775,"base":1550.720186924901,"total":9217.4380196999},{"timestamp":"2025-07-24T23:59:00+00:00","quote":7526.343748625,"base":1674.7883659499003,"total":9201.1321145749},{"timestamp":"2025-07-30T23:59:00+00:00","quote":7478.9480195749,"base":1619.2058840999998,"total":9098.1539036749},{"timestamp":"2025-08-05T23:59:00+00:00","quote":7339.6287274001,"base":1676.9039780498997,"total":9016.53270545},{"timestamp":"2025-08-11T23:59:00+00:00","quote":7384.48779365,"base":1679.2114461500005,"total":9063.6992398},{"timestamp":"2025-08-17T23:59:00+00:00","quote":7393.30990525,"base":1690.4244300998998,"total":9083.7343353499},{"timestamp":"2025-08-23T23:59:00+00:00","quote":7458.1773621001,"base":1654.9365659748992,"total":9113.113928075},{"timestamp":"2025-08-30T23:59:00+00:00","quote":7565.9131835752,"base":1526.5214495999007,"total":9092.4346331751},{"timestamp":"2025-09-05T23:59:00+00:00","quote":7439.7030971002,"base":1556.8767660250005,"total":8996.5798631252},{"timestamp":"2025-09-11T23:59:00+00:00","quote":7503.9255846253,"base":1542.2566393500001,"total":9046.1822239753},{"timestamp":"2025-09-17T23:59:00+00:00","quote":7492.4593390754,"base":1548.3599584249005,"total":9040.8192975003},{"timestamp":"2025-09-23T23:59:00+00:00","quote":7238.6936816254,"base":1544.6226635000003,"total":8783.3163451254},{"timestamp":"2025-09-29T23:59:00+00:00","quote":7172.0500755004,"base":1555.9137887999996,"total":8727.9638643004},{"timestamp":"2025-10-06T23:59:00+00:00","quote":7277.8922342504,"base":1541.6540916000004,"total":8819.5463258504},{"timestamp":"2025-10-12T23:59:00+00:00","quote":6997.4755749505,"base":1390.403814449899,"total":8387.8793894004},{"timestamp":"2025-10-18T23:59:00+00:00","quote":6785.9011988754,"base":1503.0206884000008,"total":8288.9218872754},{"timestamp":"2025-10-24T23:59:00+00:00","quote":6872.7712232004,"base":1459.497055624901,"total":8332.2682788253},{"timestamp":"2025-10-30T23:59:00+00:00","quote":6722.0796271504,"base":1422.1355977500007,"total":8144.2152249004},{"timestamp":"2025-11-05T23:59:00+00:00","quote":6611.8993478754,"base":1393.0608202000003,"total":8004.9601680754},{"timestamp":"2025-11-12T23:59:00+00:00","quote":6749.8157423005,"base":1338.3752651499008,"total":8088.1910074504},{"timestamp":"2025-11-18T23:59:00+00:00","quote":6695.4967834754,"base":1299.1121345999,"total":7994.6089180753},{"timestamp":"2025-11-24T23:59:00+00:00","quote":6638.1599263254,"base":1293.1313235748994,"total":7931.2912499003},{"timestamp":"2025-11-30T23:59:00+00:00","quote":6603.6622957503,"base":1289.7639510000008,"total":7893.4262467503},{"timestamp":"2025-12-06T23:59:00+00:00","quote":6626.5559639254,"base":1280.9477680999007,"total":7907.5037320253},{"timestamp":"2025-12-12T23:59:00+00:00","quote":6627.3025247753,"base":1267.3431791999,"total":7894.6457039752},{"timestamp":"2025-12-19T23:59:00+00:00","quote":6507.6684817504,"base":1215.0795063749001,"total":7722.7479881253},{"timestamp":"2025-12-25T23:59:00+00:00","quote":6619.8298132754,"base":1158.1441399499,"total":7777.9739532253},{"timestamp":"2025-12-31T23:59:00+00:00","quote":6616.8465030005,"base":1188.2213650999001,"total":7805.0678681004}]

Base Exposure (% of Portfolio)

Data source - GET /backtests/{id}/equity * Fields used: base_value_exit_net, quote_asset_bal, total_value_exit_net * Transform: baseExposure(equity)

What it shows. Share of total portfolio value invested in the base coin at each sample.

How to read it. 0% = fully in cash, 100% = fully invested. Areas above ~70% mean the strategy was strongly directional at that moment.

Why it matters. Quantifies risk-on / risk-off behaviour. Low average exposure with strong returns hints at high capital efficiency.

Insights (auto-derived from exposure).

Stat	Value
Peak Exposure	19.50% @ 2025-05-30 23:59:00 UTC
Min Exposure	0.00% @ 2025-01-01 23:59:00 UTC
Mean	15.67%
Std Dev	3.26%
Last	15.22%
Points	365
Across 365 points the series ranged from 0.00% (2025-01-01 23:59:00 UTC) to 19.50% (2025-05-30 23:59:00 UTC), averaging 15.67%. 100% of points were positive, 0% negative. The most recent value is 15.22%.
Showing 60 of 365 points (down-sampled for readability).

Base Exposure (% of Portfolio)

[{"timestamp":"2025-01-01T23:59:00+00:00","exposure":0},{"timestamp":"2025-01-07T23:59:00+00:00","exposure":4.314320505608136},{"timestamp":"2025-01-13T23:59:00+00:00","exposure":7.619969425739538},{"timestamp":"2025-01-20T23:59:00+00:00","exposure":10.377354575418567},{"timestamp":"2025-01-26T23:59:00+00:00","exposure":10.208532589956842},{"timestamp":"2025-02-01T23:59:00+00:00","exposure":12.142855362974728},{"timestamp":"2025-02-07T23:59:00+00:00","exposure":13.767375033570781},{"timestamp":"2025-02-13T23:59:00+00:00","exposure":13.10556826528295},{"timestamp":"2025-02-19T23:59:00+00:00","exposure":13.50946161520486},{"timestamp":"2025-02-26T23:59:00+00:00","exposure":11.889567765882342},{"timestamp":"2025-03-04T23:59:00+00:00","exposure":13.532893449943744},{"timestamp":"2025-03-10T23:59:00+00:00","exposure":13.985383475906483},{"timestamp":"2025-03-16T23:59:00+00:00","exposure":15.154238898465353},{"timestamp":"2025-03-22T23:59:00+00:00","exposure":13.59902068341122},{"timestamp":"2025-03-28T23:59:00+00:00","exposure":14.718015766322887},{"timestamp":"2025-04-04T23:59:00+00:00","exposure":16.682914556455515},{"timestamp":"2025-04-10T23:59:00+00:00","exposure":18.540484003352827},{"timestamp":"2025-04-16T23:59:00+00:00","exposure":18.419111559893047},{"timestamp":"2025-04-22T23:59:00+00:00","exposure":15.407644610175442},{"timestamp":"2025-04-28T23:59:00+00:00","exposure":16.240553534169795},{"timestamp":"2025-05-04T23:59:00+00:00","exposure":16.027619481910982},{"timestamp":"2025-05-11T23:59:00+00:00","exposure":16.01574966098339},{"timestamp":"2025-05-17T23:59:00+00:00","exposure":16.99056023156006},{"timestamp":"2025-05-23T23:59:00+00:00","exposure":17.652475560463966},{"timestamp":"2025-05-29T23:59:00+00:00","exposure":18.053733832860217},{"timestamp":"2025-06-04T23:59:00+00:00","exposure":17.397268595443016},{"timestamp":"2025-06-10T23:59:00+00:00","exposure":15.96318981471697},{"timestamp":"2025-06-17T23:59:00+00:00","exposure":18.07161314650092},{"timestamp":"2025-06-23T23:59:00+00:00","exposure":17.70187495861281},{"timestamp":"2025-06-29T23:59:00+00:00","exposure":17.608376685647514},{"timestamp":"2025-07-05T23:59:00+00:00","exposure":17.621323882079444},{"timestamp":"2025-07-11T23:59:00+00:00","exposure":17.44194469326202},{"timestamp":"2025-07-17T23:59:00+00:00","exposure":16.82376581877346},{"timestamp":"2025-07-24T23:59:00+00:00","exposure":18.201981507221046},{"timestamp":"2025-07-30T23:59:00+00:00","exposure":17.797081707377746},{"timestamp":"2025-08-05T23:59:00+00:00","exposure":18.59810231749399},{"timestamp":"2025-08-11T23:59:00+00:00","exposure":18.526778103760797},{"timestamp":"2025-08-17T23:59:00+00:00","exposure":18.609355664679793},{"timestamp":"2025-08-23T23:59:00+00:00","exposure":18.159945974959168},{"timestamp":"2025-08-30T23:59:00+00:00","exposure":16.788918603056658},{"timestamp":"2025-09-05T23:59:00+00:00","exposure":17.305206975444758},{"timestamp":"2025-09-11T23:59:00+00:00","exposure":17.048701885117048},{"timestamp":"2025-09-17T23:59:00+00:00","exposure":17.126323483237933},{"timestamp":"2025-09-23T23:59:00+00:00","exposure":17.585870789650436},{"timestamp":"2025-09-29T23:59:00+00:00","exposure":17.826767078677815},{"timestamp":"2025-10-06T23:59:00+00:00","exposure":17.47997045019604},{"timestamp":"2025-10-12T23:59:00+00:00","exposure":16.576344865031388},{"timestamp":"2025-10-18T23:59:00+00:00","exposure":18.132885179040446},{"timestamp":"2025-10-24T23:59:00+00:00","exposure":17.51620335286016},{"timestamp":"2025-10-30T23:59:00+00:00","exposure":17.46191079776373},{"timestamp":"2025-11-05T23:59:00+00:00","exposure":17.402470355263844},{"timestamp":"2025-11-12T23:59:00+00:00","exposure":16.547275700055334},{"timestamp":"2025-11-18T23:59:00+00:00","exposure":16.249852218070234},{"timestamp":"2025-11-24T23:59:00+00:00","exposure":16.304171449903},{"timestamp":"2025-11-30T23:59:00+00:00","exposure":16.339722582839013},{"timestamp":"2025-12-06T23:59:00+00:00","exposure":16.19914212511942},{"timestamp":"2025-12-12T23:59:00+00:00","exposure":16.053198923946052},{"timestamp":"2025-12-19T23:59:00+00:00","exposure":15.733771298030677},{"timestamp":"2025-12-25T23:59:00+00:00","exposure":14.890049091378755},{"timestamp":"2025-12-31T23:59:00+00:00","exposure":15.223715990429815}]

Daily Change

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net * Transform: dailyDelta(equity)

What it shows. First-difference of total_value_exit_net between consecutive equity samples (typically daily).

How to read it. Green bars are gain days, red bars are loss days. A long stretch of red bars indicates a sustained drawdown, not just a single bad day.

Why it matters. Shows whether returns are consistent or driven by a few jackpot days - important for compounding and for sizing live capital.

Insights (auto-derived from delta).

Stat	Value
Best Day	206.48 USDT @ 2025-02-08 23:59:00 UTC
Worst Day	-406.10 USDT @ 2025-10-10 23:59:00 UTC
Mean	-6.04 USDT
Std Dev	71.01 USDT
Last	-63.42 USDT
Points	364
Sum	-2,197.71 USDT
Across 364 points the series ranged from -406.10 USDT (2025-10-10 23:59:00 UTC) to 206.48 USDT (2025-02-08 23:59:00 UTC), averaging -6.04 USDT. 48% of points were positive, 52% negative. The most recent value is -63.42 USDT.
Showing 60 of 364 points (down-sampled for readability).

Daily Change

[{"timestamp":"2025-01-02T23:59:00+00:00","delta":3.5410932250997575},{"timestamp":"2025-01-08T23:59:00+00:00","delta":-39.99807825000062},{"timestamp":"2025-01-14T23:59:00+00:00","delta":24.50460117500006},{"timestamp":"2025-01-20T23:59:00+00:00","delta":-9.706871275000594},{"timestamp":"2025-01-27T23:59:00+00:00","delta":43.578287050000654},{"timestamp":"2025-02-02T23:59:00+00:00","delta":-140.8406951750003},{"timestamp":"2025-02-08T23:59:00+00:00","delta":206.47732664999967},{"timestamp":"2025-02-14T23:59:00+00:00","delta":19.607976625000447},{"timestamp":"2025-02-20T23:59:00+00:00","delta":105.62935637510054},{"timestamp":"2025-02-26T23:59:00+00:00","delta":-9.048882850000155},{"timestamp":"2025-03-05T23:59:00+00:00","delta":14.823399875000177},{"timestamp":"2025-03-11T23:59:00+00:00","delta":40.90086964999864},{"timestamp":"2025-03-17T23:59:00+00:00","delta":70.32241732500006},{"timestamp":"2025-03-23T23:59:00+00:00","delta":-42.25364454999908},{"timestamp":"2025-03-29T23:59:00+00:00","delta":-101.6846617250012},{"timestamp":"2025-04-04T23:59:00+00:00","delta":21.565561699999307},{"timestamp":"2025-04-10T23:59:00+00:00","delta":-49.20000427499872},{"timestamp":"2025-04-17T23:59:00+00:00","delta":34.179910524999286},{"timestamp":"2025-04-23T23:59:00+00:00","delta":84.56567422499938},{"timestamp":"2025-04-29T23:59:00+00:00","delta":185.53996947500127},{"timestamp":"2025-05-05T23:59:00+00:00","delta":-26.452280699999392},{"timestamp":"2025-05-11T23:59:00+00:00","delta":-71.80203027500102},{"timestamp":"2025-05-17T23:59:00+00:00","delta":-52.18681792499956},{"timestamp":"2025-05-24T23:59:00+00:00","delta":11.023771424999723},{"timestamp":"2025-05-30T23:59:00+00:00","delta":-212.10345980000056},{"timestamp":"2025-06-05T23:59:00+00:00","delta":-49.67592882499957},{"timestamp":"2025-06-11T23:59:00+00:00","delta":-85.32833934999871},{"timestamp":"2025-06-17T23:59:00+00:00","delta":-64.65662404999966},{"timestamp":"2025-06-23T23:59:00+00:00","delta":143.93656572500004},{"timestamp":"2025-06-29T23:59:00+00:00","delta":51.53644270010045},{"timestamp":"2025-07-06T23:59:00+00:00","delta":7.014533274999849},{"timestamp":"2025-07-12T23:59:00+00:00","delta":-22.09044432510018},{"timestamp":"2025-07-18T23:59:00+00:00","delta":33.80545367499872},{"timestamp":"2025-07-24T23:59:00+00:00","delta":-40.91047995000008},{"timestamp":"2025-07-30T23:59:00+00:00","delta":1.9734115499995823},{"timestamp":"2025-08-05T23:59:00+00:00","delta":-61.504858750000494},{"timestamp":"2025-08-11T23:59:00+00:00","delta":-95.115233049999},{"timestamp":"2025-08-18T23:59:00+00:00","delta":-41.78466622489941},{"timestamp":"2025-08-24T23:59:00+00:00","delta":-35.32397535000018},{"timestamp":"2025-08-30T23:59:00+00:00","delta":87.9298020999995},{"timestamp":"2025-09-05T23:59:00+00:00","delta":21.79808087499987},{"timestamp":"2025-09-11T23:59:00+00:00","delta":14.664959900099348},{"timestamp":"2025-09-17T23:59:00+00:00","delta":32.47451902499961},{"timestamp":"2025-09-24T23:59:00+00:00","delta":8.877893524899264},{"timestamp":"2025-09-30T23:59:00+00:00","delta":-30.264869425000143},{"timestamp":"2025-10-06T23:59:00+00:00","delta":56.8333007250003},{"timestamp":"2025-10-12T23:59:00+00:00","delta":120.20269062499938},{"timestamp":"2025-10-18T23:59:00+00:00","delta":27.118923200001518},{"timestamp":"2025-10-24T23:59:00+00:00","delta":14.860907324900836},{"timestamp":"2025-10-30T23:59:00+00:00","delta":-106.88037489999897},{"timestamp":"2025-11-06T23:59:00+00:00","delta":10.258892874900084},{"timestamp":"2025-11-12T23:59:00+00:00","delta":-1.1319534999993266},{"timestamp":"2025-11-18T23:59:00+00:00","delta":12.996295449899662},{"timestamp":"2025-11-24T23:59:00+00:00","delta":53.17101137499958},{"timestamp":"2025-11-30T23:59:00+00:00","delta":-4.127468074999342},{"timestamp":"2025-12-06T23:59:00+00:00","delta":36.0193936250007},{"timestamp":"2025-12-13T23:59:00+00:00","delta":-22.216551075000098},{"timestamp":"2025-12-19T23:59:00+00:00","delta":84.62647222499982},{"timestamp":"2025-12-25T23:59:00+00:00","delta":-26.210529725000015},{"timestamp":"2025-12-31T23:59:00+00:00","delta":-63.417260850000275}]

Monthly Returns

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net * Transform: monthlyReturns(equity)

What it shows. Calendar-month percentage return computed from the first vs. last equity value of each month.

How to read it. Green bars = profitable months, red bars = losing months. Look for clusters of red - they often map to specific market regimes (e.g. crypto bear quarters).

Why it matters. Calendar-month returns are the standard reporting unit for traders and funds; they make like-for-like comparison with HODL trivial.

Insights (auto-derived from returnPct).

Stat	Value
Best Month	3.18% @ 2025-04
Worst Month	-6.72% @ 2025-10
Mean	-1.88%
Std Dev	3.06%
Last	-0.01%
Points	12
Across 12 points the series ranged from -6.72% (2025-10) to 3.18% (2025-04), averaging -1.88%. 33% of points were positive, 67% negative. The most recent value is -0.01%.
Full series - 12 points.

Monthly Returns

[
  {
    "month": "2025-01",
    "returnPct": -3.094566630975401
  },
  {
    "month": "2025-02",
    "returnPct": 1.8122980908550284
  },
  {
    "month": "2025-03",
    "returnPct": -5.784524253850371
  },
  {
    "month": "2025-04",
    "returnPct": 3.1771826775308134
  },
  {
    "month": "2025-05",
    "returnPct": -4.429351662697354
  },
  {
    "month": "2025-06",
    "returnPct": -2.1039229792476886
  },
  {
    "month": "2025-07",
    "returnPct": 1.406212058685255
  },
  {
    "month": "2025-08",
    "returnPct": 0.49725926492257594
  },
  {
    "month": "2025-09",
    "returnPct": -3.3153116309357937
  },
  {
    "month": "2025-10",
    "returnPct": -6.717574182892971
  },
  {
    "month": "2025-11",
    "returnPct": -4.009122312772855
  },
  {
    "month": "2025-12",
    "returnPct": -0.009602881178808679
  }
]

Trade Analysis

Cumulative Trade Profit

Data source - GET /backtests/{id}/trades * Fields used: sell_time, profit * Transform: cumulativeProfit(trades)

What it shows. Running sum of realised per-trade profit, ordered by sell time.

How to read it. Steep upward slope => winning streak. Plateaus => many neutral or breakeven trades. Down-slopes => losses dominate.

Why it matters. Distinguishes ‘one big win + many losers’ (lottery) from ‘many small wins’ (edge). Smooth, monotonically rising curves are the dream.

Insights (auto-derived from cumProfit).

Stat	Value
Peak Cum.	516.31 USDT @ 2025-12-31
Trough Cum.	2.78 USDT @ 2025-01-01
Mean	303.94 USDT
Std Dev	145.63 USDT
Last	516.31 USDT
Points	365
Sum	110,937.31 USDT
Across 365 points the series ranged from 2.78 USDT (2025-01-01) to 516.31 USDT (2025-12-31), averaging 303.94 USDT. 100% of points were positive, 0% negative. The most recent value is 516.31 USDT.
Showing 60 of 365 points (down-sampled for readability).

Cumulative Trade Profit

[{"timestamp":"2025-01-01","cumProfit":2.778316},{"timestamp":"2025-01-07","cumProfit":18.159253000000003},{"timestamp":"2025-01-13","cumProfit":32.75659},{"timestamp":"2025-01-20","cumProfit":48.718903},{"timestamp":"2025-01-26","cumProfit":60.047247},{"timestamp":"2025-02-01","cumProfit":71.315674},{"timestamp":"2025-02-07","cumProfit":85.055171},{"timestamp":"2025-02-13","cumProfit":96.64666700000001},{"timestamp":"2025-02-19","cumProfit":107.567396},{"timestamp":"2025-02-26","cumProfit":123.98322200000003},{"timestamp":"2025-03-04","cumProfit":133.35465299999998},{"timestamp":"2025-03-10","cumProfit":141.31668799999997},{"timestamp":"2025-03-16","cumProfit":151.77361299999998},{"timestamp":"2025-03-22","cumProfit":164.011581},{"timestamp":"2025-03-28","cumProfit":169.47693199999998},{"timestamp":"2025-04-04","cumProfit":179.460447},{"timestamp":"2025-04-10","cumProfit":188.846139},{"timestamp":"2025-04-16","cumProfit":197.57536399999998},{"timestamp":"2025-04-22","cumProfit":212.42055399999998},{"timestamp":"2025-04-28","cumProfit":225.35708},{"timestamp":"2025-05-04","cumProfit":241.75909199999998},{"timestamp":"2025-05-11","cumProfit":256.485139},{"timestamp":"2025-05-17","cumProfit":268.72572900000006},{"timestamp":"2025-05-23","cumProfit":279.7368760000001},{"timestamp":"2025-05-29","cumProfit":289.87167200000005},{"timestamp":"2025-06-04","cumProfit":300.592098},{"timestamp":"2025-06-10","cumProfit":311.07208099999997},{"timestamp":"2025-06-17","cumProfit":316.711542},{"timestamp":"2025-06-23","cumProfit":323.78288299999997},{"timestamp":"2025-06-29","cumProfit":329.32438799999994},{"timestamp":"2025-07-05","cumProfit":333.60478599999993},{"timestamp":"2025-07-11","cumProfit":337.25247199999995},{"timestamp":"2025-07-17","cumProfit":348.853525},{"timestamp":"2025-07-24","cumProfit":364.896565},{"timestamp":"2025-07-30","cumProfit":372.99672699999996},{"timestamp":"2025-08-05","cumProfit":379.850282},{"timestamp":"2025-08-11","cumProfit":384.3635119999999},{"timestamp":"2025-08-17","cumProfit":388.0805979999999},{"timestamp":"2025-08-23","cumProfit":392.51436299999995},{"timestamp":"2025-08-30","cumProfit":399.5576219999999},{"timestamp":"2025-09-05","cumProfit":404.15586799999994},{"timestamp":"2025-09-11","cumProfit":408.0008059999999},{"timestamp":"2025-09-17","cumProfit":411.6576929999999},{"timestamp":"2025-09-23","cumProfit":417.1653449999999},{"timestamp":"2025-09-29","cumProfit":422.1303049999999},{"timestamp":"2025-10-06","cumProfit":426.73459199999985},{"timestamp":"2025-10-12","cumProfit":435.62024699999984},{"timestamp":"2025-10-18","cumProfit":445.1273579999998},{"timestamp":"2025-10-24","cumProfit":451.57831299999975},{"timestamp":"2025-10-30","cumProfit":456.5717939999997},{"timestamp":"2025-11-05","cumProfit":462.34551499999975},{"timestamp":"2025-11-12","cumProfit":469.7261799999997},{"timestamp":"2025-11-18","cumProfit":476.3915249999998},{"timestamp":"2025-11-24","cumProfit":482.3479029999997},{"timestamp":"2025-11-30","cumProfit":484.28748099999973},{"timestamp":"2025-12-06","cumProfit":490.0866379999997},{"timestamp":"2025-12-12","cumProfit":493.65921699999967},{"timestamp":"2025-12-19","cumProfit":500.2060399999997},{"timestamp":"2025-12-25","cumProfit":507.1763369999997},{"timestamp":"2025-12-31","cumProfit":516.3140109999997}]

Daily Trade Profit

Data source - GET /backtests/{id}/trades * Fields used: sell_time, profit * Transform: dailyTradeProfit(trades)

What it shows. Aggregated realised profit per calendar day across all trades.

How to read it. Tall green bars are blockbuster days, red bars are pain days. Variance here drives the equity volatility above.

Why it matters. Helps spot regime-dependence - e.g. all profit concentrated on Mondays, or all losses during weekend pumps.

Insights (auto-derived from profit).

Stat	Value
Best Day	5.48 USDT @ 2025-04-18
Worst Day	0.2065 USDT @ 2025-11-26
Mean	1.41 USDT
Std Dev	0.8771 USDT
Last	2.83 USDT
Points	365
Sum	516.31 USDT
Across 365 points the series ranged from 0.2065 USDT (2025-11-26) to 5.48 USDT (2025-04-18), averaging 1.41 USDT. 100% of points were positive, 0% negative. The most recent value is 2.83 USDT.
Showing 60 of 365 points (down-sampled for readability).

Daily Trade Profit

[{"day":"2025-01-01","profit":2.778316},{"day":"2025-01-07","profit":1.753423},{"day":"2025-01-13","profit":2.231425},{"day":"2025-01-20","profit":2.57487},{"day":"2025-01-26","profit":1.703984},{"day":"2025-02-01","profit":1.01064},{"day":"2025-02-07","profit":2.423318},{"day":"2025-02-13","profit":0.968054},{"day":"2025-02-19","profit":1.957588},{"day":"2025-02-26","profit":1.747499},{"day":"2025-03-04","profit":2.162012},{"day":"2025-03-10","profit":2.074497},{"day":"2025-03-16","profit":1.622635},{"day":"2025-03-22","profit":1.738324},{"day":"2025-03-28","profit":0.904915},{"day":"2025-04-04","profit":1.844465},{"day":"2025-04-10","profit":0.704365},{"day":"2025-04-16","profit":1.536494},{"day":"2025-04-22","profit":1.769873},{"day":"2025-04-28","profit":2.852449},{"day":"2025-05-04","profit":2.816432},{"day":"2025-05-11","profit":1.234075},{"day":"2025-05-17","profit":2.371885},{"day":"2025-05-23","profit":1.644847},{"day":"2025-05-29","profit":1.42159},{"day":"2025-06-04","profit":2.833068},{"day":"2025-06-10","profit":1.307738},{"day":"2025-06-17","profit":0.832228},{"day":"2025-06-23","profit":1.236366},{"day":"2025-06-29","profit":0.575946},{"day":"2025-07-05","profit":0.513406},{"day":"2025-07-11","profit":1.40502},{"day":"2025-07-17","profit":1.66724},{"day":"2025-07-24","profit":2.564349},{"day":"2025-07-30","profit":1.081827},{"day":"2025-08-05","profit":0.622236},{"day":"2025-08-11","profit":1.0669},{"day":"2025-08-17","profit":0.276129},{"day":"2025-08-23","profit":0.384143},{"day":"2025-08-30","profit":1.075705},{"day":"2025-09-05","profit":0.681391},{"day":"2025-09-11","profit":1.027227},{"day":"2025-09-17","profit":0.703433},{"day":"2025-09-23","profit":0.957814},{"day":"2025-09-29","profit":1.012759},{"day":"2025-10-06","profit":0.545007},{"day":"2025-10-12","profit":1.367367},{"day":"2025-10-18","profit":0.869824},{"day":"2025-10-24","profit":0.624508},{"day":"2025-10-30","profit":1.029967},{"day":"2025-11-05","profit":1.179018},{"day":"2025-11-12","profit":1.175883},{"day":"2025-11-18","profit":0.743328},{"day":"2025-11-24","profit":0.411056},{"day":"2025-11-30","profit":0.374584},{"day":"2025-12-06","profit":0.371963},{"day":"2025-12-12","profit":0.867171},{"day":"2025-12-19","profit":0.640159},{"day":"2025-12-25","profit":0.629798},{"day":"2025-12-31","profit":2.834991}]

Trades per Day

Data source - GET /backtests/{id}/trades * Fields used: sell_time * Transform: tradesPerDay(trades)

What it shows. How many trades closed on each calendar day.

How to read it. High bars = busy days (often during high volatility). Empty days = strategy sat on its hands.

Why it matters. Sanity check on activity. Strategies that fire 200+ trades/day will be fee-bled in live; <1 trade/week may be too sparse to trust statistically.

Insights (auto-derived from count).

Stat	Value
Busiest Day	632 trades @ 2025-01-02
Quietest Day	13 trades @ 2025-10-25
Mean	187 trades
Std Dev	124 trades
Last	256 trades
Points	365
Sum	68077 trades
Across 365 points the series ranged from 13 trades (2025-10-25) to 632 trades (2025-01-02), averaging 187 trades. The most recent value is 256 trades.
Showing 60 of 365 points (down-sampled for readability).

Trades per Day

[{"day":"2025-01-01","count":452},{"day":"2025-01-07","count":281},{"day":"2025-01-13","count":380},{"day":"2025-01-20","count":419},{"day":"2025-01-26","count":259},{"day":"2025-02-01","count":149},{"day":"2025-02-07","count":419},{"day":"2025-02-13","count":159},{"day":"2025-02-19","count":246},{"day":"2025-02-26","count":298},{"day":"2025-03-04","count":339},{"day":"2025-03-10","count":301},{"day":"2025-03-16","count":199},{"day":"2025-03-22","count":248},{"day":"2025-03-28","count":113},{"day":"2025-04-04","count":171},{"day":"2025-04-10","count":110},{"day":"2025-04-16","count":269},{"day":"2025-04-22","count":169},{"day":"2025-04-28","count":289},{"day":"2025-05-04","count":330},{"day":"2025-05-11","count":191},{"day":"2025-05-17","count":273},{"day":"2025-05-23","count":204},{"day":"2025-05-29","count":151},{"day":"2025-06-04","count":283},{"day":"2025-06-10","count":130},{"day":"2025-06-17","count":139},{"day":"2025-06-23","count":166},{"day":"2025-06-29","count":80},{"day":"2025-07-05","count":76},{"day":"2025-07-11","count":257},{"day":"2025-07-17","count":310},{"day":"2025-07-24","count":447},{"day":"2025-07-30","count":167},{"day":"2025-08-05","count":89},{"day":"2025-08-11","count":178},{"day":"2025-08-17","count":43},{"day":"2025-08-23","count":60},{"day":"2025-08-30","count":157},{"day":"2025-09-05","count":90},{"day":"2025-09-11","count":144},{"day":"2025-09-17","count":93},{"day":"2025-09-23","count":96},{"day":"2025-09-29","count":96},{"day":"2025-10-06","count":55},{"day":"2025-10-12","count":132},{"day":"2025-10-18","count":52},{"day":"2025-10-24","count":38},{"day":"2025-10-30","count":185},{"day":"2025-11-05","count":142},{"day":"2025-11-12","count":176},{"day":"2025-11-18","count":88},{"day":"2025-11-24","count":42},{"day":"2025-11-30","count":39},{"day":"2025-12-06","count":38},{"day":"2025-12-12","count":82},{"day":"2025-12-19","count":48},{"day":"2025-12-25","count":54},{"day":"2025-12-31","count":256}]

Cumulative Fees

Data source - GET /backtests/{id}/trades * Fields used: sell_time, buy_fee_in_quote, sell_fee_in_quote * Transform: cumulativeFees(trades)

What it shows. Running sum of buy + sell fees across all trades, in quote currency.

How to read it. A nearly-linear curve confirms fees scale with trade count. Steep slopes warn of an over-trading strategy.

Why it matters. Compare the final value to total realised profit - if fees >= 50% of gross profit, the strategy is paying the exchange more than the trader.

Insights (auto-derived from cumFees).

Stat	Value
Final Fees	516.44 USDT @ 2025-12-31
Start	3.46 USDT @ 2025-01-01
Mean	321.91 USDT
Std Dev	144.47 USDT
Last	516.44 USDT
Points	365
Sum	117,497.51 USDT
Across 365 points the series ranged from 3.46 USDT (2025-01-01) to 516.44 USDT (2025-12-31), averaging 321.91 USDT. 100% of points were positive, 0% negative. The most recent value is 516.44 USDT.
Showing 60 of 365 points (down-sampled for readability).

Cumulative Fees

[{"index":0,"timestamp":"2025-01-01","cumFees":3.458684},{"index":6,"timestamp":"2025-01-07","cumFees":22.838547},{"index":12,"timestamp":"2025-01-13","cumFees":41.14431},{"index":19,"timestamp":"2025-01-20","cumFees":61.157596999999996},{"index":25,"timestamp":"2025-01-26","cumFees":75.15805299999998},{"index":31,"timestamp":"2025-02-01","cumFees":88.82652599999997},{"index":37,"timestamp":"2025-02-07","cumFees":105.26416999999996},{"index":43,"timestamp":"2025-02-13","cumFees":117.86575199999996},{"index":49,"timestamp":"2025-02-19","cumFees":128.96309699999995},{"index":56,"timestamp":"2025-02-26","cumFees":148.53607099999994},{"index":62,"timestamp":"2025-03-04","cumFees":158.73817499999993},{"index":68,"timestamp":"2025-03-10","cumFees":166.77985399999994},{"index":74,"timestamp":"2025-03-16","cumFees":176.89122899999998},{"index":80,"timestamp":"2025-03-22","cumFees":188.76516099999998},{"index":86,"timestamp":"2025-03-28","cumFees":194.29150999999996},{"index":93,"timestamp":"2025-04-04","cumFees":202.11179499999997},{"index":99,"timestamp":"2025-04-10","cumFees":212.03933999999998},{"index":105,"timestamp":"2025-04-16","cumFees":223.136915},{"index":111,"timestamp":"2025-04-22","cumFees":235.164426},{"index":117,"timestamp":"2025-04-28","cumFees":245.47310000000002},{"index":123,"timestamp":"2025-05-04","cumFees":261.0900890000001},{"index":130,"timestamp":"2025-05-11","cumFees":275.1949420000001},{"index":136,"timestamp":"2025-05-17","cumFees":287.9363390000001},{"index":142,"timestamp":"2025-05-23","cumFees":297.9518920000001},{"index":148,"timestamp":"2025-05-29","cumFees":306.2208960000001},{"index":154,"timestamp":"2025-06-04","cumFees":316.8100700000001},{"index":160,"timestamp":"2025-06-10","cumFees":326.3540870000001},{"index":167,"timestamp":"2025-06-17","cumFees":332.7144260000001},{"index":173,"timestamp":"2025-06-23","cumFees":340.72325700000005},{"index":179,"timestamp":"2025-06-29","cumFees":346.53775200000007},{"index":185,"timestamp":"2025-07-05","cumFees":351.143154},{"index":191,"timestamp":"2025-07-11","cumFees":355.773468},{"index":197,"timestamp":"2025-07-17","cumFees":369.550015},{"index":204,"timestamp":"2025-07-24","cumFees":384.24267499999996},{"index":210,"timestamp":"2025-07-30","cumFees":394.81801299999995},{"index":216,"timestamp":"2025-08-05","cumFees":402.01445799999993},{"index":222,"timestamp":"2025-08-11","cumFees":407.5080289999999},{"index":228,"timestamp":"2025-08-17","cumFees":412.04654299999993},{"index":234,"timestamp":"2025-08-23","cumFees":416.80297799999994},{"index":241,"timestamp":"2025-08-30","cumFees":424.44872000000004},{"index":247,"timestamp":"2025-09-05","cumFees":429.1432740000001},{"index":253,"timestamp":"2025-09-11","cumFees":433.0953360000001},{"index":259,"timestamp":"2025-09-17","cumFees":436.8582490000001},{"index":265,"timestamp":"2025-09-23","cumFees":441.95905000000005},{"index":271,"timestamp":"2025-09-29","cumFees":445.50534300000004},{"index":278,"timestamp":"2025-10-06","cumFees":448.83416500000004},{"index":284,"timestamp":"2025-10-12","cumFees":454.82938000000007},{"index":290,"timestamp":"2025-10-18","cumFees":460.4659690000001},{"index":296,"timestamp":"2025-10-24","cumFees":464.1101140000001},{"index":302,"timestamp":"2025-10-30","cumFees":468.39223300000015},{"index":308,"timestamp":"2025-11-05","cumFees":474.3967030000001},{"index":315,"timestamp":"2025-11-12","cumFees":482.2572620000002},{"index":321,"timestamp":"2025-11-18","cumFees":488.5543500000002},{"index":327,"timestamp":"2025-11-24","cumFees":493.3062940000002},{"index":333,"timestamp":"2025-11-30","cumFees":494.88027200000016},{"index":339,"timestamp":"2025-12-06","cumFees":499.06014500000015},{"index":345,"timestamp":"2025-12-12","cumFees":501.7853950000002},{"index":352,"timestamp":"2025-12-19","cumFees":505.9665990000002},{"index":358,"timestamp":"2025-12-25","cumFees":510.3287060000001},{"index":364,"timestamp":"2025-12-31","cumFees":516.4447960000002}]

Profit Distribution (%)

Data source - GET /backtests/{id}/trades * Fields used: profit_percentage * Transform: profitDistribution(trades, 30)

What it shows. Histogram of per-trade percentage returns, binned across the observed range.

How to read it. A long left-tail (deep losses) with a short right-tail screams ‘pick up pennies in front of a steamroller’. Tight, right-shifted bins are healthy.

Why it matters. Distribution shape matters more than the average - payoff asymmetry decides whether the strategy survives black-swan markets.

Insights (auto-derived from count).

Stat	Value
Most Populated Bin	6054 trades @ 0.10%
Least Populated Bin	11 trades @ 0.26%
Mean	1135 trades
Std Dev	1341 trades
Last	156 trades
Points	60
Sum	68077 trades
Across 60 points the series ranged from 11 trades (0.26%) to 6054 trades (0.10%), averaging 1135 trades. The most recent value is 156 trades.
Full series - 60 points.

Profit Distribution (%)

[{"bin":"0.10%","count":6054,"from":0.099812},{"bin":"0.10%","count":4752,"from":0.103955},{"bin":"0.11%","count":3376,"from":0.108098},{"bin":"0.11%","count":2982,"from":0.112241},{"bin":"0.12%","count":2740,"from":0.116384},{"bin":"0.12%","count":2690,"from":0.120526},{"bin":"0.12%","count":2668,"from":0.124669},{"bin":"0.13%","count":2556,"from":0.128812},{"bin":"0.13%","count":2633,"from":0.132955},{"bin":"0.14%","count":2285,"from":0.137097},{"bin":"0.14%","count":2921,"from":0.14124},{"bin":"0.15%","count":2453,"from":0.145383},{"bin":"0.15%","count":2970,"from":0.149526},{"bin":"0.15%","count":3125,"from":0.153669},{"bin":"0.16%","count":2974,"from":0.157811},{"bin":"0.16%","count":1769,"from":0.161954},{"bin":"0.17%","count":1569,"from":0.166097},{"bin":"0.17%","count":1431,"from":0.17024},{"bin":"0.17%","count":1284,"from":0.174382},{"bin":"0.18%","count":1388,"from":0.178525},{"bin":"0.18%","count":1360,"from":0.182668},{"bin":"0.19%","count":1149,"from":0.186811},{"bin":"0.19%","count":1395,"from":0.190954},{"bin":"0.20%","count":923,"from":0.195096},{"bin":"0.20%","count":926,"from":0.199239},{"bin":"0.20%","count":884,"from":0.203382},{"bin":"0.21%","count":920,"from":0.207525},{"bin":"0.21%","count":920,"from":0.211667},{"bin":"0.22%","count":889,"from":0.21581},{"bin":"0.22%","count":1072,"from":0.219953},{"bin":"0.22%","count":298,"from":0.224096},{"bin":"0.23%","count":243,"from":0.228239},{"bin":"0.23%","count":394,"from":0.232381},{"bin":"0.24%","count":263,"from":0.236524},{"bin":"0.24%","count":148,"from":0.240667},{"bin":"0.24%","count":157,"from":0.24481},{"bin":"0.25%","count":103,"from":0.248952},{"bin":"0.25%","count":33,"from":0.253095},{"bin":"0.26%","count":19,"from":0.257238},{"bin":"0.26%","count":11,"from":0.261381},{"bin":"0.27%","count":12,"from":0.265524},{"bin":"0.27%","count":17,"from":0.269666},{"bin":"0.27%","count":12,"from":0.273809},{"bin":"0.28%","count":31,"from":0.277952},{"bin":"0.28%","count":16,"from":0.282095},{"bin":"0.29%","count":46,"from":0.286237},{"bin":"0.29%","count":65,"from":0.29038},{"bin":"0.29%","count":95,"from":0.294523},{"bin":"0.30%","count":92,"from":0.298666},{"bin":"0.30%","count":84,"from":0.302809},{"bin":"0.31%","count":81,"from":0.306951},{"bin":"0.31%","count":67,"from":0.311094},{"bin":"0.32%","count":64,"from":0.315237},{"bin":"0.32%","count":46,"from":0.31938},{"bin":"0.32%","count":77,"from":0.323522},{"bin":"0.33%","count":60,"from":0.327665},{"bin":"0.33%","count":79,"from":0.331808},{"bin":"0.34%","count":148,"from":0.335951},{"bin":"0.34%","count":102,"from":0.340094},{"bin":"0.34%","count":156,"from":0.344236}]

Rolling Win Rate (50 Trades)

Data source - GET /backtests/{id}/trades * Fields used: profit * Transform: rollingWinRate(trades, 50)

What it shows. Percentage of profitable trades within a sliding window of the last 50 closed trades.

How to read it. Above 50% line => recent edge holds. Sustained dips below => regime change or strategy decay.

Why it matters. Win rate alone is meaningless (you can have 90% wins and still lose money). Rolling win rate spots when the edge breaks over time.

Insights (auto-derived from winRate).

Stat	Value
Hottest Window	100.00% @ 680
Coldest Window	100.00% @ 680
Mean	100.00%
Std Dev	0.00%
Last	100.00%
Points	1000
Across 1000 points the series ranged from 100.00% (680) to 100.00% (680), averaging 100.00%. 100% of points were positive, 0% negative. The most recent value is 100.00%.
Showing 60 of 1000 points (down-sampled for readability).

Rolling Win Rate (50 Trades)

[{"index":680,"winRate":100},{"index":1827,"winRate":100},{"index":2974,"winRate":100},{"index":4121,"winRate":100},{"index":5268,"winRate":100},{"index":6414,"winRate":100},{"index":7561,"winRate":100},{"index":8708,"winRate":100},{"index":9788,"winRate":100},{"index":10935,"winRate":100},{"index":12081,"winRate":100},{"index":13228,"winRate":100},{"index":14375,"winRate":100},{"index":15522,"winRate":100},{"index":16669,"winRate":100},{"index":17816,"winRate":100},{"index":18963,"winRate":100},{"index":20110,"winRate":100},{"index":21257,"winRate":100},{"index":22404,"winRate":100},{"index":23550,"winRate":100},{"index":24697,"winRate":100},{"index":25844,"winRate":100},{"index":26924,"winRate":100},{"index":28071,"winRate":100},{"index":29217,"winRate":100},{"index":30364,"winRate":100},{"index":31511,"winRate":100},{"index":32658,"winRate":100},{"index":33805,"winRate":100},{"index":34952,"winRate":100},{"index":36099,"winRate":100},{"index":37246,"winRate":100},{"index":38393,"winRate":100},{"index":39540,"winRate":100},{"index":40686,"winRate":100},{"index":41833,"winRate":100},{"index":42913,"winRate":100},{"index":44060,"winRate":100},{"index":45207,"winRate":100},{"index":46353,"winRate":100},{"index":47500,"winRate":100},{"index":48647,"winRate":100},{"index":49794,"winRate":100},{"index":50941,"winRate":100},{"index":52088,"winRate":100},{"index":53235,"winRate":100},{"index":54382,"winRate":100},{"index":55529,"winRate":100},{"index":56676,"winRate":100},{"index":57822,"winRate":100},{"index":58969,"winRate":100},{"index":60049,"winRate":100},{"index":61196,"winRate":100},{"index":62343,"winRate":100},{"index":63489,"winRate":100},{"index":64636,"winRate":100},{"index":65783,"winRate":100},{"index":66930,"winRate":100},{"index":68077,"winRate":100}]

Risk & Recovery

Gross vs. Net PnL (Fee Impact)

Data source - GET /backtests/{id}/trades * Fields used: sell_time, profit, buy_fee_in_quote, sell_fee_in_quote * Transform: grossVsNetPnL(trades)

What it shows. Two cumulative-profit lines: gross (before subtracting fees) vs. net (the actual realised profit).

How to read it. The vertical gap between gross and net is exactly what the exchange earned. Wider gaps => heavier fee drag.

Why it matters. Quantifies the fee tax on edge. Useful when comparing fee-tier upgrades or maker-vs-taker variants.

Insights (auto-derived from net).

Stat	Value
Peak Net	1.01 USDT @ 2025-12-31 23:58:00 UTC
Trough Net	0.0050 USDT @ 2025-01-01 00:05:00 UTC
Mean	0.4551 USDT
Std Dev	0.3102 USDT
Last	1.01 USDT
Points	100
Sum	45.51 USDT
Across 100 points the series ranged from 0.0050 USDT (2025-01-01 00:05:00 UTC) to 1.01 USDT (2025-12-31 23:58:00 UTC), averaging 0.4551 USDT. 100% of points were positive, 0% negative. The most recent value is 1.01 USDT.
Showing 60 of 100 points (down-sampled for readability).

Gross vs. Net PnL (Fee Impact)

[{"timestamp":"2025-01-01T00:05:00Z","net":0.0050337,"gross":0.0126},{"timestamp":"2025-01-01T00:07:00Z","net":0.0151389,"gross":0.0378},{"timestamp":"2025-01-01T00:08:00Z","net":0.02018835,"gross":0.0504},{"timestamp":"2025-01-01T00:10:00Z","net":0.032288025000000005,"gross":0.0777},{"timestamp":"2025-01-01T00:14:00Z","net":0.046432575000000004,"gross":0.10710000000000001},{"timestamp":"2025-01-01T00:15:00Z","net":0.0534765,"gross":0.12180000000000002},{"timestamp":"2025-01-01T00:24:00Z","net":0.065648625,"gross":0.1491},{"timestamp":"2025-01-01T00:24:00Z","net":0.075744375,"gross":0.1743},{"timestamp":"2025-01-01T00:25:00Z","net":0.08284815000000001,"gross":0.189},{"timestamp":"2025-01-01T00:27:00Z","net":0.0970053,"gross":0.21839999999999998},{"timestamp":"2025-01-01T00:28:00Z","net":0.11110260000000001,"gross":0.24779999999999996},{"timestamp":"2025-01-01T00:31:00Z","net":0.11821267500000002,"gross":0.26249999999999996},{"timestamp":"2025-01-01T00:38:00Z","net":0.132423375,"gross":0.29189999999999994},{"timestamp":"2025-01-01T00:50:00Z","net":0.146602575,"gross":0.3212999999999999},{"timestamp":"2025-01-01T00:50:00Z","net":0.15368430000000002,"gross":0.3359999999999999},{"timestamp":"2025-01-15T22:54:00Z","net":0.16570310000000002,"gross":0.3631999999999999},{"timestamp":"2025-01-15T23:38:00Z","net":0.17568435000000002,"gross":0.38819999999999993},{"timestamp":"2025-01-16T12:56:00Z","net":0.18566560000000001,"gross":0.41319999999999996},{"timestamp":"2025-10-10T21:44:00Z","net":0.190656225,"gross":0.42569999999999997},{"timestamp":"2025-10-11T03:21:00Z","net":0.21310935,"gross":0.4632},{"timestamp":"2025-10-11T03:39:00Z","net":0.24803435,"gross":0.5132},{"timestamp":"2025-10-11T19:37:00Z","net":0.253024975,"gross":0.5257},{"timestamp":"2025-10-11T19:51:00Z","net":0.26300622500000004,"gross":0.5506999999999999},{"timestamp":"2025-10-12T08:02:00Z","net":0.297931225,"gross":0.6006999999999999},{"timestamp":"2025-10-17T06:32:00Z","net":0.315393725,"gross":0.6256999999999999},{"timestamp":"2025-10-17T20:46:00Z","net":0.32537497500000007,"gross":0.6506999999999998},{"timestamp":"2025-10-18T01:10:00Z","net":0.36029997500000005,"gross":0.7006999999999999},{"timestamp":"2025-10-18T14:18:00Z","net":0.37776247500000004,"gross":0.7256999999999999},{"timestamp":"2025-10-19T03:19:00Z","net":0.412687475,"gross":0.7757},{"timestamp":"2025-10-19T05:17:00Z","net":0.447612475,"gross":0.8257},{"timestamp":"2025-10-19T05:21:00Z","net":0.465074975,"gross":0.8507},{"timestamp":"2025-10-19T09:42:00Z","net":0.4875281,"gross":0.8882},{"timestamp":"2025-10-22T05:28:00Z","net":0.5224531000000001,"gross":0.9382},{"timestamp":"2025-10-22T11:03:00Z","net":0.5274437250000001,"gross":0.9507},{"timestamp":"2025-10-22T18:05:00Z","net":0.5498968500000002,"gross":0.9882},{"timestamp":"2025-10-22T19:50:00Z","net":0.5598781000000003,"gross":1.0131999999999999},{"timestamp":"2025-10-22T20:02:00Z","net":0.5648687250000003,"gross":1.0256999999999998},{"timestamp":"2025-10-23T00:43:00Z","net":0.5748499750000003,"gross":1.0506999999999997},{"timestamp":"2025-10-24T16:42:00Z","net":0.5973031000000004,"gross":1.0881999999999996},{"timestamp":"2025-10-24T16:43:00Z","net":0.6022937250000004,"gross":1.1006999999999996},{"timestamp":"2025-10-29T13:02:00Z","net":0.6372187250000005,"gross":1.1506999999999994},{"timestamp":"2025-10-29T17:58:00Z","net":0.6471999750000006,"gross":1.1756999999999993},{"timestamp":"2025-10-29T18:01:00Z","net":0.6646624750000006,"gross":1.2006999999999992},{"timestamp":"2025-10-29T20:00:00Z","net":0.6871156000000007,"gross":1.238199999999999},{"timestamp":"2025-11-08T16:10:00Z","net":0.7220406000000008,"gross":1.288199999999999},{"timestamp":"2025-12-31T20:17:00Z","net":0.7454679250000008,"gross":1.326699999999999},{"timestamp":"2025-12-31T20:25:00Z","net":0.7570473000000008,"gross":1.345799999999999},{"timestamp":"2025-12-31T20:29:00Z","net":0.7801774000000007,"gross":1.3839999999999988},{"timestamp":"2025-12-31T20:45:00Z","net":0.8033969250000007,"gross":1.4222999999999988},{"timestamp":"2025-12-31T20:49:00Z","net":0.8150657250000007,"gross":1.441499999999999},{"timestamp":"2025-12-31T21:05:00Z","net":0.8382244750000006,"gross":1.4796999999999987},{"timestamp":"2025-12-31T21:10:00Z","net":0.8613545750000006,"gross":1.5178999999999985},{"timestamp":"2025-12-31T21:14:00Z","net":0.8729339500000005,"gross":1.5369999999999984},{"timestamp":"2025-12-31T21:19:00Z","net":0.8960640500000004,"gross":1.5751999999999982},{"timestamp":"2025-12-31T21:23:00Z","net":0.9191655000000004,"gross":1.613399999999998},{"timestamp":"2025-12-31T21:30:00Z","net":0.9307162250000004,"gross":1.6324999999999978},{"timestamp":"2025-12-31T22:05:00Z","net":0.9538463250000003,"gross":1.6706999999999976},{"timestamp":"2025-12-31T22:25:00Z","net":0.9769477750000003,"gross":1.7088999999999974},{"timestamp":"2025-12-31T22:48:00Z","net":0.9884985000000003,"gross":1.7279999999999973},{"timestamp":"2025-12-31T23:58:00Z","net":1.0116286000000003,"gross":1.766199999999997}]

Rolling Sharpe Ratio (30d / 90d)

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net * Transform: rollingSharpe(equity, 30) + rollingSharpe(equity, 90)

What it shows. Annualised risk-adjusted return computed over rolling 30-day and 90-day windows.

How to read it. Above 1 = solid, above 2 = excellent, below 0 = losing money on risk-adjusted basis. Compare 30d vs 90d to see short vs medium-term consistency.

Why it matters. A flat-to-rising rolling Sharpe is the gold standard for production-ready strategies; a collapsing Sharpe predicts upcoming drawdowns.

Insights (auto-derived from sharpe30).

Stat	Value
Peak Sharpe (30d)	5.47 @ 2025-05-10 23:59:00 UTC
Worst Sharpe (30d)	-7.17 @ 2025-02-06 23:59:00 UTC
Mean	-1.75
Std Dev	2.48
Last	-0.01
Points	335
Across 335 points the series ranged from -7.17 (2025-02-06 23:59:00 UTC) to 5.47 (2025-05-10 23:59:00 UTC), averaging -1.75. The most recent value is -0.01.
Showing 60 of 335 points (down-sampled for readability).

Rolling Sharpe Ratio (30d / 90d)

[{"timestamp":"2025-01-31T23:59:00+00:00","sharpe30":-4.241850075575013,"sharpe90":-2.7804055305510147},{"timestamp":"2025-02-06T23:59:00+00:00","sharpe30":-7.1695948876195645,"sharpe90":-3.384722404903448},{"timestamp":"2025-02-11T23:59:00+00:00","sharpe30":-3.825531005742683,"sharpe90":-2.2903511408895856},{"timestamp":"2025-02-17T23:59:00+00:00","sharpe30":-2.0934666248831273,"sharpe90":-1.7497393328833792},{"timestamp":"2025-02-23T23:59:00+00:00","sharpe30":0.8123856435858907,"sharpe90":-1.0835327826419534},{"timestamp":"2025-02-28T23:59:00+00:00","sharpe30":0.5664995407386986,"sharpe90":-0.5848709580191931},{"timestamp":"2025-03-06T23:59:00+00:00","sharpe30":1.57876837560573,"sharpe90":0.08042260832248772},{"timestamp":"2025-03-12T23:59:00+00:00","sharpe30":-1.3931025002682151,"sharpe90":0.29064568569596133},{"timestamp":"2025-03-17T23:59:00+00:00","sharpe30":-1.1426198226758277,"sharpe90":0.06028145965792455},{"timestamp":"2025-03-23T23:59:00+00:00","sharpe30":-3.71740844378765,"sharpe90":-0.5235065092343377},{"timestamp":"2025-03-29T23:59:00+00:00","sharpe30":-5.507383346455405,"sharpe90":-1.0071490526396771},{"timestamp":"2025-04-03T23:59:00+00:00","sharpe30":-5.891059229586147,"sharpe90":-1.0514294392415826},{"timestamp":"2025-04-09T23:59:00+00:00","sharpe30":-2.8095263023582686,"sharpe90":-0.2767349774541656},{"timestamp":"2025-04-15T23:59:00+00:00","sharpe30":-1.443786682499067,"sharpe90":-0.41324630765533943},{"timestamp":"2025-04-20T23:59:00+00:00","sharpe30":-1.1180957399833633,"sharpe90":-0.6546659528901081},{"timestamp":"2025-04-26T23:59:00+00:00","sharpe30":-0.570757910889048,"sharpe90":-1.0834866640142613},{"timestamp":"2025-05-02T23:59:00+00:00","sharpe30":3.886820143027738,"sharpe90":-0.3583982671982853},{"timestamp":"2025-05-07T23:59:00+00:00","sharpe30":3.645490305603644,"sharpe90":0.5367978361937004},{"timestamp":"2025-05-13T23:59:00+00:00","sharpe30":4.456901619959251,"sharpe90":0.37515738329647114},{"timestamp":"2025-05-19T23:59:00+00:00","sharpe30":1.3419356040029249,"sharpe90":-0.03004417408106644},{"timestamp":"2025-05-24T23:59:00+00:00","sharpe30":0.41606720094632665,"sharpe90":-0.1551657144791359},{"timestamp":"2025-05-30T23:59:00+00:00","sharpe30":-2.598917553205145,"sharpe90":-0.9572728192574775},{"timestamp":"2025-06-05T23:59:00+00:00","sharpe30":-0.3101015047087398,"sharpe90":-0.6714064234474684},{"timestamp":"2025-06-10T23:59:00+00:00","sharpe30":-2.1946206074718266,"sharpe90":-1.3354730534633674},{"timestamp":"2025-06-16T23:59:00+00:00","sharpe30":-2.6962933560487925,"sharpe90":-1.0589408175158426},{"timestamp":"2025-06-22T23:59:00+00:00","sharpe30":-4.175410164957774,"sharpe90":-1.1114963422556752},{"timestamp":"2025-06-27T23:59:00+00:00","sharpe30":-3.6451423143642057,"sharpe90":-1.0399284244307954},{"timestamp":"2025-07-03T23:59:00+00:00","sharpe30":-1.0914604112983846,"sharpe90":-0.6678177351634439},{"timestamp":"2025-07-09T23:59:00+00:00","sharpe30":-1.543329491624978,"sharpe90":-0.8944444192635553},{"timestamp":"2025-07-14T23:59:00+00:00","sharpe30":1.307600918940157,"sharpe90":0.05052271936833993},{"timestamp":"2025-07-20T23:59:00+00:00","sharpe30":2.695903065982662,"sharpe90":0.026082089367394624},{"timestamp":"2025-07-25T23:59:00+00:00","sharpe30":2.483946073910724,"sharpe90":-0.6245191666834915},{"timestamp":"2025-07-31T23:59:00+00:00","sharpe30":1.1446197551818245,"sharpe90":-0.5269478457615605},{"timestamp":"2025-08-06T23:59:00+00:00","sharpe30":0.1552846295556867,"sharpe90":-0.851425581750694},{"timestamp":"2025-08-11T23:59:00+00:00","sharpe30":-0.8322553150300667,"sharpe90":-2.330705765282946},{"timestamp":"2025-08-17T23:59:00+00:00","sharpe30":-1.5512479983601015,"sharpe90":-2.5951464733191503},{"timestamp":"2025-08-23T23:59:00+00:00","sharpe30":-0.842294091950094,"sharpe90":-2.668301921377962},{"timestamp":"2025-08-28T23:59:00+00:00","sharpe30":-0.15830492458861223,"sharpe90":-2.2457003042937878},{"timestamp":"2025-09-03T23:59:00+00:00","sharpe30":-0.4158467113559292,"sharpe90":-2.4789365947145034},{"timestamp":"2025-09-09T23:59:00+00:00","sharpe30":-1.406661639301202,"sharpe90":-2.8018449956510323},{"timestamp":"2025-09-14T23:59:00+00:00","sharpe30":-0.13810232558189897,"sharpe90":-2.8658798490295565},{"timestamp":"2025-09-20T23:59:00+00:00","sharpe30":0.03157259455411109,"sharpe90":-2.9658848019673982},{"timestamp":"2025-09-26T23:59:00+00:00","sharpe30":-3.034251481182024,"sharpe90":-3.233144687681564},{"timestamp":"2025-10-01T23:59:00+00:00","sharpe30":-2.786025994078735,"sharpe90":-3.401236236201214},{"timestamp":"2025-10-07T23:59:00+00:00","sharpe30":-3.08946892317396,"sharpe90":-3.266883249701857},{"timestamp":"2025-10-13T23:59:00+00:00","sharpe30":-3.73235647531819,"sharpe90":-3.6126411166199586},{"timestamp":"2025-10-18T23:59:00+00:00","sharpe30":-5.051286594658852,"sharpe90":-4.124699220960796},{"timestamp":"2025-10-24T23:59:00+00:00","sharpe30":-3.0931689953550348,"sharpe90":-3.0338111047592315},{"timestamp":"2025-10-30T23:59:00+00:00","sharpe30":-3.7531448057422434,"sharpe90":-2.8245745106159412},{"timestamp":"2025-11-04T23:59:00+00:00","sharpe30":-5.179369262004498,"sharpe90":null},{"timestamp":"2025-11-10T23:59:00+00:00","sharpe30":-1.0445012013855364,"sharpe90":null},{"timestamp":"2025-11-16T23:59:00+00:00","sharpe30":-2.7073567302370014,"sharpe90":null},{"timestamp":"2025-11-21T23:59:00+00:00","sharpe30":-4.0900118780909125,"sharpe90":null},{"timestamp":"2025-11-27T23:59:00+00:00","sharpe30":-3.4970145807879947,"sharpe90":null},{"timestamp":"2025-12-03T23:59:00+00:00","sharpe30":-1.0189947987501355,"sharpe90":null},{"timestamp":"2025-12-08T23:59:00+00:00","sharpe30":-3.6436433751721014,"sharpe90":null},{"timestamp":"2025-12-14T23:59:00+00:00","sharpe30":-2.8286354148314667,"sharpe90":null},{"timestamp":"2025-12-20T23:59:00+00:00","sharpe30":-2.5079803291974545,"sharpe90":null},{"timestamp":"2025-12-25T23:59:00+00:00","sharpe30":-2.1161816872269443,"sharpe90":null},{"timestamp":"2025-12-31T23:59:00+00:00","sharpe30":-0.009762992198897395,"sharpe90":null}]

Drawdown Recovery Episodes

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net * Transform: drawdownRecoveryEpisodes(equity)

What it shows. Each point is one drawdown episode: x-axis = duration in days, y-axis = depth in %.

How to read it. Top-left = quick shallow dips (good). Bottom-right = long deep underwater periods (bad). The single worst point dominates investor PTSD.

Why it matters. Investors care about how long they sat underwater as much as how deep. This chart surfaces both at once.

Insights (auto-derived from depth).

Stat	Value
Shallowest	-23.69% @ 358
Deepest Episode	-23.69% @ 358
Mean	-23.69%
Std Dev	0.00%
Last	-23.69%
Points	1
Across 1 points the series ranged from -23.69% (358) to -23.69% (358), averaging -23.69%. 0% of points were positive, 100% negative. The most recent value is -23.69%.
Full series - 1 points.

Drawdown Recovery Episodes

[{"start":"2025-01-07T23:59:00+00:00","end":"ongoing","depth":-23.68693384147357,"durationDays":358}]

Consecutive Win/Loss Streaks

Data source - GET /backtests/{id}/trades * Fields used: sell_time, profit * Transform: consecutiveStreaks(trades)

What it shows. Length of every uninterrupted run of wins (green) and losses (red) in chronological order.

How to read it. Long red bars are the painful streaks; long green bars are the lucky ones. The longest red streak sets the realistic position-sizing limit.

Why it matters. Behaviour during a 10-trade losing streak determines if a real trader would still be running the bot - most are not.

Insights (auto-derived from length).

Stat	Value
Longest Streak	100 trades @ win
Shortest Streak	100 trades @ win
Mean	100 trades
Std Dev	0 trades
Last	100 trades
Points	1
Sum	100 trades
Across 1 points the series ranged from 100 trades (win) to 100 trades (win), averaging 100 trades. The most recent value is 100 trades.
Full series - 1 points.

Consecutive Win/Loss Streaks

[{"type":"win","length":100,"index":0}]

Capital Utilization (% Invested)

Data source - GET /backtests/{id}/equity * Fields used: base_value_exit_net, total_value_exit_net * Transform: baseExposure(equity)

What it shows. Same series as Base Exposure, re-framed as ‘how much working capital was actually deployed’.

How to read it. Average level = average capital efficiency. 30% average means the strategy could in theory run 3x the notional with the same cash.

Why it matters. Two strategies with identical returns are not equal - the one using less capital is far more attractive for portfolio composition.

Insights (auto-derived from exposure).

Stat	Value
Peak Utilization	19.50% @ 2025-05-30 23:59:00 UTC
Min Utilization	0.00% @ 2025-01-01 23:59:00 UTC
Mean	15.67%
Std Dev	3.26%
Last	15.22%
Points	365
Across 365 points the series ranged from 0.00% (2025-01-01 23:59:00 UTC) to 19.50% (2025-05-30 23:59:00 UTC), averaging 15.67%. 100% of points were positive, 0% negative. The most recent value is 15.22%.
Showing 60 of 365 points (down-sampled for readability).

Capital Utilization (% Invested)

[{"timestamp":"2025-01-01T23:59:00+00:00","exposure":0},{"timestamp":"2025-01-07T23:59:00+00:00","exposure":4.314320505608136},{"timestamp":"2025-01-13T23:59:00+00:00","exposure":7.619969425739538},{"timestamp":"2025-01-20T23:59:00+00:00","exposure":10.377354575418567},{"timestamp":"2025-01-26T23:59:00+00:00","exposure":10.208532589956842},{"timestamp":"2025-02-01T23:59:00+00:00","exposure":12.142855362974728},{"timestamp":"2025-02-07T23:59:00+00:00","exposure":13.767375033570781},{"timestamp":"2025-02-13T23:59:00+00:00","exposure":13.10556826528295},{"timestamp":"2025-02-19T23:59:00+00:00","exposure":13.50946161520486},{"timestamp":"2025-02-26T23:59:00+00:00","exposure":11.889567765882342},{"timestamp":"2025-03-04T23:59:00+00:00","exposure":13.532893449943744},{"timestamp":"2025-03-10T23:59:00+00:00","exposure":13.985383475906483},{"timestamp":"2025-03-16T23:59:00+00:00","exposure":15.154238898465353},{"timestamp":"2025-03-22T23:59:00+00:00","exposure":13.59902068341122},{"timestamp":"2025-03-28T23:59:00+00:00","exposure":14.718015766322887},{"timestamp":"2025-04-04T23:59:00+00:00","exposure":16.682914556455515},{"timestamp":"2025-04-10T23:59:00+00:00","exposure":18.540484003352827},{"timestamp":"2025-04-16T23:59:00+00:00","exposure":18.419111559893047},{"timestamp":"2025-04-22T23:59:00+00:00","exposure":15.407644610175442},{"timestamp":"2025-04-28T23:59:00+00:00","exposure":16.240553534169795},{"timestamp":"2025-05-04T23:59:00+00:00","exposure":16.027619481910982},{"timestamp":"2025-05-11T23:59:00+00:00","exposure":16.01574966098339},{"timestamp":"2025-05-17T23:59:00+00:00","exposure":16.99056023156006},{"timestamp":"2025-05-23T23:59:00+00:00","exposure":17.652475560463966},{"timestamp":"2025-05-29T23:59:00+00:00","exposure":18.053733832860217},{"timestamp":"2025-06-04T23:59:00+00:00","exposure":17.397268595443016},{"timestamp":"2025-06-10T23:59:00+00:00","exposure":15.96318981471697},{"timestamp":"2025-06-17T23:59:00+00:00","exposure":18.07161314650092},{"timestamp":"2025-06-23T23:59:00+00:00","exposure":17.70187495861281},{"timestamp":"2025-06-29T23:59:00+00:00","exposure":17.608376685647514},{"timestamp":"2025-07-05T23:59:00+00:00","exposure":17.621323882079444},{"timestamp":"2025-07-11T23:59:00+00:00","exposure":17.44194469326202},{"timestamp":"2025-07-17T23:59:00+00:00","exposure":16.82376581877346},{"timestamp":"2025-07-24T23:59:00+00:00","exposure":18.201981507221046},{"timestamp":"2025-07-30T23:59:00+00:00","exposure":17.797081707377746},{"timestamp":"2025-08-05T23:59:00+00:00","exposure":18.59810231749399},{"timestamp":"2025-08-11T23:59:00+00:00","exposure":18.526778103760797},{"timestamp":"2025-08-17T23:59:00+00:00","exposure":18.609355664679793},{"timestamp":"2025-08-23T23:59:00+00:00","exposure":18.159945974959168},{"timestamp":"2025-08-30T23:59:00+00:00","exposure":16.788918603056658},{"timestamp":"2025-09-05T23:59:00+00:00","exposure":17.305206975444758},{"timestamp":"2025-09-11T23:59:00+00:00","exposure":17.048701885117048},{"timestamp":"2025-09-17T23:59:00+00:00","exposure":17.126323483237933},{"timestamp":"2025-09-23T23:59:00+00:00","exposure":17.585870789650436},{"timestamp":"2025-09-29T23:59:00+00:00","exposure":17.826767078677815},{"timestamp":"2025-10-06T23:59:00+00:00","exposure":17.47997045019604},{"timestamp":"2025-10-12T23:59:00+00:00","exposure":16.576344865031388},{"timestamp":"2025-10-18T23:59:00+00:00","exposure":18.132885179040446},{"timestamp":"2025-10-24T23:59:00+00:00","exposure":17.51620335286016},{"timestamp":"2025-10-30T23:59:00+00:00","exposure":17.46191079776373},{"timestamp":"2025-11-05T23:59:00+00:00","exposure":17.402470355263844},{"timestamp":"2025-11-12T23:59:00+00:00","exposure":16.547275700055334},{"timestamp":"2025-11-18T23:59:00+00:00","exposure":16.249852218070234},{"timestamp":"2025-11-24T23:59:00+00:00","exposure":16.304171449903},{"timestamp":"2025-11-30T23:59:00+00:00","exposure":16.339722582839013},{"timestamp":"2025-12-06T23:59:00+00:00","exposure":16.19914212511942},{"timestamp":"2025-12-12T23:59:00+00:00","exposure":16.053198923946052},{"timestamp":"2025-12-19T23:59:00+00:00","exposure":15.733771298030677},{"timestamp":"2025-12-25T23:59:00+00:00","exposure":14.890049091378755},{"timestamp":"2025-12-31T23:59:00+00:00","exposure":15.223715990429815}]

Deep Dive

Price Chart with Buy/Sell Markers

Data source - GET /backtests/{id}/trades * Fields used: buy_time, buy_price, sell_time, sell_price * Transform: priceWithMarkers(trades)

What it shows. Reconstructed price line of the traded asset with green dots for buys and red dots for sells.

How to read it. Buys clustered near local lows + sells near highs => good market timing. Buys near local highs => classic FOMO behaviour.

Why it matters. The most intuitive sanity check on whether the strategy is actually buying low and selling high vs. simply riding momentum.

Insights (auto-derived from price).

Stat	Value
Highest Price	2.44 @ 2025-01-01 00:52:00 UTC
Lowest Price	0.2600 @ 2025-12-31 20:07:00 UTC
Mean	0.8784
Std Dev	0.8735
Last	0.2630
Points	159
Across 159 points the series ranged from 0.2600 (2025-12-31 20:07:00 UTC) to 2.44 (2025-01-01 00:52:00 UTC), averaging 0.8784. The most recent value is 0.2630.
Showing 60 of 159 points (down-sampled for readability).

Price Chart with Buy/Sell Markers

[{"timestamp":"2025-01-01T00:01:00Z","price":2.427},{"timestamp":"2025-01-01T00:06:00Z","price":2.396},{"timestamp":"2025-01-01T00:08:00Z","price":2.4},{"timestamp":"2025-01-01T00:11:00Z","price":2.422},{"timestamp":"2025-01-01T00:16:00Z","price":2.429},{"timestamp":"2025-01-01T00:19:00Z","price":2.408},{"timestamp":"2025-01-01T00:23:00Z","price":2.398},{"timestamp":"2025-01-01T00:26:00Z","price":2.413},{"timestamp":"2025-01-01T00:28:00Z","price":2.436},{"timestamp":"2025-01-01T00:33:00Z","price":2.421},{"timestamp":"2025-01-01T00:38:00Z","price":2.417},{"timestamp":"2025-01-01T00:41:00Z","price":2.404},{"timestamp":"2025-01-01T00:52:00Z","price":2.439},{"timestamp":"2025-01-15T23:02:00Z","price":2},{"timestamp":"2025-01-15T23:07:00Z","price":2},{"timestamp":"2025-01-16T12:50:00Z","price":2.005},{"timestamp":"2025-10-10T21:44:00Z","price":0.401},{"timestamp":"2025-10-11T03:20:00Z","price":0.401},{"timestamp":"2025-10-11T03:34:00Z","price":0.401},{"timestamp":"2025-10-11T03:39:00Z","price":0.403},{"timestamp":"2025-10-11T19:38:00Z","price":0.401},{"timestamp":"2025-10-11T19:51:00Z","price":0.401},{"timestamp":"2025-10-17T06:32:00Z","price":0.401},{"timestamp":"2025-10-17T20:43:00Z","price":0.401},{"timestamp":"2025-10-17T20:46:00Z","price":0.401},{"timestamp":"2025-10-18T01:10:00Z","price":0.403},{"timestamp":"2025-10-19T00:18:00Z","price":0.401},{"timestamp":"2025-10-19T02:59:00Z","price":0.401},{"timestamp":"2025-10-19T04:33:00Z","price":0.403},{"timestamp":"2025-10-19T05:21:00Z","price":0.403},{"timestamp":"2025-10-19T08:18:00Z","price":0.401},{"timestamp":"2025-10-21T04:30:00Z","price":0.401},{"timestamp":"2025-10-22T05:28:00Z","price":0.403},{"timestamp":"2025-10-22T11:09:00Z","price":0.4},{"timestamp":"2025-10-22T18:05:00Z","price":0.403},{"timestamp":"2025-10-22T19:43:00Z","price":0.4},{"timestamp":"2025-10-22T20:02:00Z","price":0.401},{"timestamp":"2025-10-23T00:43:00Z","price":0.401},{"timestamp":"2025-10-24T16:41:00Z","price":0.4},{"timestamp":"2025-10-24T16:43:00Z","price":0.401},{"timestamp":"2025-10-29T11:31:00Z","price":0.401},{"timestamp":"2025-10-29T17:46:00Z","price":0.4},{"timestamp":"2025-10-29T17:57:00Z","price":0.4},{"timestamp":"2025-10-29T18:07:00Z","price":0.4},{"timestamp":"2025-10-29T20:00:00Z","price":0.403},{"timestamp":"2025-10-29T21:03:00Z","price":0.401},{"timestamp":"2025-12-31T14:12:00Z","price":0.263},{"timestamp":"2025-12-31T20:14:00Z","price":0.261},{"timestamp":"2025-12-31T20:25:00Z","price":0.263},{"timestamp":"2025-12-31T20:29:00Z","price":0.263},{"timestamp":"2025-12-31T20:45:00Z","price":0.261},{"timestamp":"2025-12-31T21:04:00Z","price":0.263},{"timestamp":"2025-12-31T21:06:00Z","price":0.264},{"timestamp":"2025-12-31T21:13:00Z","price":0.262},{"timestamp":"2025-12-31T21:19:00Z","price":0.264},{"timestamp":"2025-12-31T21:23:00Z","price":0.263},{"timestamp":"2025-12-31T22:04:00Z","price":0.262},{"timestamp":"2025-12-31T22:23:00Z","price":0.263},{"timestamp":"2025-12-31T22:48:00Z","price":0.263},{"timestamp":"2025-12-31T23:58:00Z","price":0.263}]

Total markers: 100 buys, 100 sells (UI caps each at 500 dots).

Trade Size Distribution (Notional)

Data source - GET /backtests/{id}/trades * Fields used: buy_price, buy_quantity * Transform: tradeSizeDistribution(trades, 25)

What it shows. Histogram of per-trade notional value (buy_price x buy_quantity) in quote currency.

How to read it. A tight peak => uniform sizing. A long right-tail => a few oversized positions that dominate PnL and risk.

Why it matters. Position-size variance is a hidden risk factor - strategies that ape into one mega-trade are not as repeatable as their headline numbers suggest.

Insights (auto-derived from count).

Stat	Value
Most Populated Bin	25 trades @ $5.00
Least Populated Bin	1 trades @ $5.02
Mean	4 trades
Std Dev	7 trades
Last	1 trades
Points	25
Sum	100 trades
Across 25 points the series ranged from 1 trades ($5.02) to **25 trades** ($5.00), averaging 4 trades. The most recent value is 1 trades.
Full series - 25 points.

Trade Size Distribution (Notional)

[{"bin":"$5.00","count":25,"from":5},{"bin":"$5.00","count":9,"from":5.004},{"bin":"$5.01","count":3,"from":5.011},{"bin":"$5.01","count":25,"from":5.013},{"bin":"$5.02","count":1,"from":5.018},{"bin":"$5.02","count":1,"from":5.019},{"bin":"$5.02","count":15,"from":5.023},{"bin":"$5.03","count":1,"from":5.027},{"bin":"$5.03","count":1,"from":5.032},{"bin":"$5.03","count":1,"from":5.034},{"bin":"$5.04","count":1,"from":5.038},{"bin":"$5.05","count":2,"from":5.048},{"bin":"$5.05","count":1,"from":5.051},{"bin":"$5.05","count":1,"from":5.053},{"bin":"$5.06","count":1,"from":5.057},{"bin":"$5.06","count":1,"from":5.061},{"bin":"$5.07","count":1,"from":5.067},{"bin":"$5.07","count":1,"from":5.069},{"bin":"$5.07","count":1,"from":5.072},{"bin":"$5.08","count":1,"from":5.08},{"bin":"$5.08","count":1,"from":5.084},{"bin":"$5.09","count":2,"from":5.086},{"bin":"$5.10","count":2,"from":5.097},{"bin":"$5.10","count":1,"from":5.101},{"bin":"$5.11","count":1,"from":5.107}]

Return Distribution by Fill Type

Data source - GET /backtests/{id}/trades * Fields used: fill_type, profit_percentage * Transform: returnByFillType(trades)

What it shows. Stacked histogram showing how TP (take-profit), SL (stop-loss) and TSL (trailing stop-loss) fills are distributed across return bins.

How to read it. Healthy strategies have TP fills bunched on the right (positive returns) and SL/TSL fills bunched on the left. Overlap between them indicates fuzzy exit logic.

Why it matters. Confirms each exit mechanism does its job. If SL fills land in positive territory, the stop is probably too tight; if TP fills land in red, the order routing is broken.

Insights (auto-derived from TP).

Stat	Value
Largest TP Bin	33 trades @ 0.1
Smallest TP Bin	0 trades @ 0.11
Mean	5 trades
Std Dev	10 trades
Last	25 trades
Points	20
Sum	100 trades
Across 20 points the series ranged from 0 trades (0.11) to 33 trades (0.1), averaging 5 trades. The most recent value is 25 trades.
Full series - 20 points.

Return Distribution by Fill Type

[
  {
    "bin": 0.1,
    "TP": 33,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.11,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.12,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.14,
    "TP": 17,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.15,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.16,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.17,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.19,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.2,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.21,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.22,
    "TP": 25,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.24,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.25,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.26,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.27,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.29,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.3,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.31,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.32,
    "TP": 0,
    "SL": 0,
    "TSL": 0
  },
  {
    "bin": 0.34,
    "TP": 25,
    "SL": 0,
    "TSL": 0
  }
]

Monthly Equity Comparison (YoY)

Data source - GET /backtests/{id}/equity * Fields used: timestamp, total_value_exit_net * Transform: monthlyEquityYoY(equity)

What it shows. Equity rebased to 100 at the start of each calendar year, plotted month-by-month so years overlay.

How to read it. Lines that consistently end above 100 => profitable in every plotted year. Crossover with the 100 reference line tells you when the year flipped from green to red.

Why it matters. Year-over-year overlay is the fastest visual test for regime stability - strategies that worked only in 2021 will stand out clearly.

Insights (auto-derived from 2025).

Stat	Value
Best Indexed Value	97.30 @ Feb
Worst Indexed Value	78.03 @ Dec
Mean	88.91
Std Dev	6.13
Last	78.03
Points	12
Across 12 points the series ranged from 78.03 (Dec) to 97.30 (Feb), averaging 88.91. The most recent value is 78.03.
Full series - 12 points.

Monthly Equity Comparison (YoY)

[
  {
    "2025": 96.90543336902459,
    "month": "Jan"
  },
  {
    "2025": 97.30206577627416,
    "month": "Feb"
  },
  {
    "2025": 91.18918683621716,
    "month": "Mar"
  },
  {
    "2025": 94.3252907905622,
    "month": "Apr"
  },
  {
    "2025": 91.32074210925943,
    "month": "May"
  },
  {
    "2025": 89.8549831196161,
    "month": "Jun"
  },
  {
    "2025": 90.09777228462264,
    "month": "Jul"
  },
  {
    "2025": 90.19883384753295,
    "month": "Aug"
  },
  {
    "2025": 86.95283170787418,
    "month": "Sep"
  },
  {
    "2025": 81.85549804528749,
    "month": "Oct"
  },
  {
    "2025": 78.91233812949724,
    "month": "Nov"
  },
  {
    "2025": 78.02899976227481,
    "month": "Dec"
  }
]

Advanced

Profit by Weekday

Data source - GET /backtests/{id}/trades * Fields used: sell_time, profit * Transform: profitByWeekday(trades)

What it shows. Sum of realised profit aggregated by ISO weekday (Mon-Sun).

How to read it. Bars above zero => winning days of the week, bars below => losing days. Crypto often shows distinct weekend behaviour.

Why it matters. Surfaces calendar effects that a strategy may inadvertently exploit (or be exposed to). Useful for time-based filters in production.

Insights (auto-derived from profit).

Stat	Value
Best Weekday	82.84 USDT @ Sun
Worst Weekday	59.07 USDT @ Sat
Mean	73.76 USDT
Std Dev	9.15 USDT
Last	82.84 USDT
Points	7
Sum	516.31 USDT
Across 7 points the series ranged from 59.07 USDT (Sat) to 82.84 USDT (Sun), averaging 73.76 USDT. 100% of points were positive, 0% negative. The most recent value is 82.84 USDT.
Full series - 7 points.

Profit by Weekday

[
  {
    "day": "Mon",
    "profit": 80.445597
  },
  {
    "day": "Tue",
    "profit": 78.608777
  },
  {
    "day": "Wed",
    "profit": 71.557215
  },
  {
    "day": "Thu",
    "profit": 82.075477
  },
  {
    "day": "Fri",
    "profit": 61.712417
  },
  {
    "day": "Sat",
    "profit": 59.072544
  },
  {
    "day": "Sun",
    "profit": 82.841985
  }
]

Profit by Hour (UTC)

Data source - GET /backtests/{id}/trades * Fields used: sell_time, profit * Transform: profitByHour(trades)

What it shows. Sum of realised profit aggregated by hour-of-day (UTC), 0-23.

How to read it. Clusters around US/EU/Asia trading sessions reveal which liquidity window the strategy actually monetises.

Why it matters. Lets you build a time-of-day filter or schedule the live bot to only run during profitable hours, cutting fees and infra cost.

Insights (auto-derived from profit).

Stat	Value
Best Hour (UTC)	33.24 USDT @ 0:00
Worst Hour (UTC)	16.91 USDT @ 23:00
Mean	21.51 USDT
Std Dev	4.07 USDT
Last	16.91 USDT
Points	24
Sum	516.31 USDT
Across 24 points the series ranged from 16.91 USDT (23:00) to 33.24 USDT (0:00), averaging 21.51 USDT. 100% of points were positive, 0% negative. The most recent value is 16.91 USDT.
Full series - 24 points.

Profit by Hour (UTC)

[{"hour":"0:00","profit":33.241895},{"hour":"1:00","profit":24.970952},{"hour":"2:00","profit":22.301616},{"hour":"3:00","profit":19.889641},{"hour":"4:00","profit":18.51342},{"hour":"5:00","profit":17.602623},{"hour":"6:00","profit":18.128292},{"hour":"7:00","profit":20.268807},{"hour":"8:00","profit":21.568489},{"hour":"9:00","profit":20.034375},{"hour":"10:00","profit":17.064666},{"hour":"11:00","profit":19.26007},{"hour":"12:00","profit":22.666001},{"hour":"13:00","profit":26.552539},{"hour":"14:00","profit":28.201118},{"hour":"15:00","profit":26.525593},{"hour":"16:00","profit":25.793342},{"hour":"17:00","profit":23.193757},{"hour":"18:00","profit":20.215267},{"hour":"19:00","profit":18.351042},{"hour":"20:00","profit":17.724571},{"hour":"21:00","profit":18.892571},{"hour":"22:00","profit":18.446609},{"hour":"23:00","profit":16.906754}]

Profit vs. Hold Time

Data source - GET /backtests/{id}/trades * Fields used: buy_time, sell_time, profit_percentage * Transform: holdTimes(trades)

What it shows. Scatter plot: x = trade hold duration in hours, y = realised return %.

How to read it. Concentration in the upper-left (short hold + positive return) is ideal - fast turnover with edge. Lower-right (long hold + loss) means the strategy bag-holds losers.

Why it matters. Tests whether time-in-market correlates with profitability. Strategies with no relationship are usually noise-driven.

Insights (auto-derived from profitPct).

Stat	Value
Best Trade Return	0.35% @ 0.016666666666666666
Worst Trade Return	0.10% @ 1.4166666666666667
Mean	0.20%
Std Dev	0.10%
Last	0.23%
Points	100
Across 100 points the series ranged from 0.10% (1.4166666666666667) to 0.35% (0.016666666666666666), averaging 0.20%. 100% of points were positive, 0% negative. The most recent value is 0.23%.
Showing 60 of 100 points (down-sampled for readability).

Profit vs. Hold Time

[{"hours":0.15,"profitPct":0.1382056036,"profit":0.007043925},{"hours":0,"profitPct":0.0999166319,"profit":0.0050337},{"hours":0,"profitPct":0.1002295492,"profit":0.00504315},{"hours":0.016666666666666666,"profitPct":0.1004385965,"profit":0.00504945},{"hours":0.05,"profitPct":0.138800578,"profit":0.007059675},{"hours":0,"profitPct":0.1382056036,"profit":0.007043925},{"hours":0.18333333333333332,"profitPct":0.138800578,"profit":0.007059675},{"hours":0.1,"profitPct":0.1404796512,"profit":0.007103775},{"hours":0.06666666666666667,"profitPct":0.1409629784,"profit":0.007116375},{"hours":0.016666666666666666,"profitPct":0.1006479933,"profit":0.00505575},{"hours":0,"profitPct":0.1398777455,"profit":0.007088025},{"hours":0.38333333333333336,"profitPct":0.1376130757,"profit":0.007028175},{"hours":0.2833333333333333,"profitPct":0.1389198678,"profit":0.007062825},{"hours":0.03333333333333333,"profitPct":0.1408419958,"profit":0.007113225},{"hours":0.18333333333333332,"profitPct":0.1396376812,"profit":0.007081725},{"hours":1.4166666666666667,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.5166666666666667,"profitPct":0.0998125,"profit":0.004990625},{"hours":0,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.016666666666666666,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.016666666666666666,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.03333333333333333,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.05,"profitPct":0.0998125,"profit":0.004990625},{"hours":0,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.21666666666666667,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.03333333333333333,"profitPct":0.0998125,"profit":0.004990625},{"hours":2.8,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.13333333333333333,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.3333333333333333,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.21666666666666667,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.06666666666666667,"profitPct":0.3483790524,"profit":0.0174625},{"hours":1.3333333333333333,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.03333333333333333,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.3333333333333333,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.11666666666666667,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.2,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.016666666666666666,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.016666666666666666,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.016666666666666666,"profitPct":0.0998125,"profit":0.004990625},{"hours":1.5166666666666666,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.1,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.016666666666666666,"profitPct":0.0998125,"profit":0.004990625},{"hours":0.75,"profitPct":0.3483790524,"profit":0.0174625},{"hours":235.11666666666667,"profitPct":0.3483790524,"profit":0.0174625},{"hours":0.36666666666666664,"profitPct":0.2328544061,"profit":0.0116688},{"hours":0.11666666666666667,"profitPct":0.2343269231,"profit":0.011758525},{"hours":0.016666666666666666,"profitPct":0.2313931298,"profit":0.011579375},{"hours":0.55,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0,"profitPct":0.2328544061,"profit":0.0116688},{"hours":0.23333333333333334,"profitPct":0.2313931298,"profit":0.011579375},{"hours":0.21666666666666667,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0.03333333333333333,"profitPct":0.2313931298,"profit":0.011579375},{"hours":0.016666666666666666,"profitPct":0.2313931298,"profit":0.011579375},{"hours":0,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0.45,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0.4,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0.03333333333333333,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0.5333333333333333,"profitPct":0.2299429658,"profit":0.011550725},{"hours":0.016666666666666666,"profitPct":0.2313931298,"profit":0.011579375}]

Fill Type Breakdown

Data source - GET /backtests/{id}/trades * Fields used: fill_type * Transform: computeKPIs(equity, trades).fillTypes

What it shows. Counts of how many trades closed via TP (take-profit), SL (stop-loss) or TSL (trailing stop-loss).

How to read it. TP-dominant => targets do most of the work. SL-dominant => the strategy bleeds via stops. TSL-dominant => relies heavily on trend-following.

Why it matters. Tells you how profit was made, not just how much. Two strategies with the same return but different fill mixes have very different live behaviour.

Insights (auto-derived from count).

Stat	Value
Most Frequent Exit	100 trades @ TP
Least Frequent Exit	0 trades @ SL
Mean	33 trades
Std Dev	47 trades
Last	0 trades
Points	3
Sum	100 trades
Across 3 points the series ranged from 0 trades (SL) to 100 trades (TP), averaging 33 trades. The most recent value is 0 trades.
Full series - 3 points.

Fill Type Breakdown

[
  {
    "type": "TP",
    "count": 100
  },
  {
    "type": "SL",
    "count": 0
  },
  {
    "type": "TSL",
    "count": 0
  }
]

Machine-Readable Facts

This section is optimized for LLM/RAG consumption. Every metric appears as both a flat key/value row and as canonical JSON so a model can quote exact values without re-deriving them. The JSON below is the exact object whose SHA-256 is published as facts_sha256 in the frontmatter - re-hash it to verify.

JSON

facts.json

{
  "backtest_id": "14c346cf-9049-46a6-ac75-e17a2e31b31c",
  "symbol": "AGLDUSDT",
  "strategy": "3LongTimeLong.json",
  "strategy_label": "LongTimeLong",
  "status": "completed",
  "period_from": "2025-01-01",
  "period_to": "2025-12-31",
  "duration_days": 364,
  "start_balance_usdt": 10000,
  "final_value_usdt": 7805.0678681,
  "return_pct": -21.9493,
  "total_profit_usdt": 516.31401182,
  "avg_profit_per_trade_usdt": 0.00758427,
  "fulfilled_trades": 68077,
  "active_orders": 736,
  "win_rate_pct": 100,
  "best_trade_usdt": 0.0174625,
  "worst_trade_usdt": 0.00499063,
  "trades_per_month": 5676.96936688035,
  "max_drawdown_pct": 23.68693384147357,
  "max_drawdown_abs_usdt": 2370.8086717997994,
  "drawdown_duration_days": 349,
  "recovery_days": null,
  "cagr_pct": -22.015714364737203,
  "volatility_annualized_pct": 15.054449019971974,
  "sharpe_annualized": -1.5763389865112414,
  "sortino_annualized": -1.446884625415156,
  "time_in_market_pct": 99.72602739726028,
  "final_cash_usdt": 6616.8465030005,
  "final_base_value_usdt": null,
  "final_unrealized_pnl_usdt": -2540.3158420751,
  "median_hold_hours": 0.1,
  "avg_hold_hours": 3.632730923694779,
  "buyhold_return_pct": null,
  "outperformance_vs_buyhold_pp": null
}

Flat Table

Key	Value
backtest_id	14c346cf-9049-46a6-ac75-e17a2e31b31c
symbol	AGLDUSDT
strategy	3LongTimeLong.json
strategy_label	LongTimeLong
status	completed
period_from	2025-01-01
period_to	2025-12-31
duration_days	364
start_balance_usdt	10000
final_value_usdt	7805.0678681
return_pct	-21.9493
total_profit_usdt	516.31401182
avg_profit_per_trade_usdt	0.00758427
fulfilled_trades	68077
active_orders	736
win_rate_pct	100
best_trade_usdt	0.0174625
worst_trade_usdt	0.00499063
trades_per_month	5676.96936688035
max_drawdown_pct	23.68693384147357
max_drawdown_abs_usdt	2370.8086717997994
drawdown_duration_days	349
recovery_days	null
cagr_pct	-22.015714364737203
volatility_annualized_pct	15.054449019971974
sharpe_annualized	-1.5763389865112414
sortino_annualized	-1.446884625415156
time_in_market_pct	99.72602739726028
final_cash_usdt	6616.8465030005
final_base_value_usdt	null
final_unrealized_pnl_usdt	-2540.3158420751
median_hold_hours	0.1
avg_hold_hours	3.632730923694779
buyhold_return_pct	null
outperformance_vs_buyhold_pp	null

Metric Provenance Index

For every headline metric in this report, this table answers three questions: where did the value come from, what JSON field, and what transform (if any) was applied. Use it to audit any number - or to teach an LLM to cite values verbatim.

Metric	Source	JSON path	Transform	Value in this report
Symbol	GET /backtests/{id}	$.symbol	direct API value	AGLDUSDT
Mode	GET /backtests/{id}	$.mode_name	direct API value	3LongTimeLong.json
Status	GET /backtests/{id}	$.status	direct API value	completed
Period start	GET /backtests/{id}	$.config.from	direct API value	2025-01-01 00:00:01
Period end	GET /backtests/{id}	$.config.to	direct API value	2025-12-31 23:59:59
Start balance	GET /backtests/{id}	$.start_balance	direct API value	10,000.00 USDT
Final value	GET /backtests/{id}	$.final_value	direct API value	7,805.07 USDT
Return %	GET /backtests/{id}	$.return_pct	direct API value (cross-check: (final_value/start_balance - 1) * 100)	-21.95%
Total profit	GET /backtests/{id}	$.total_profit	direct API value (cross-check: Sum trades[].profit)	+516.31 USDT
Win rate %	GET /backtests/{id}	$.win_rate	direct API value (cross-check: 100 * count(trades[].profit > 0) / count(trades))	100.0%
Trades count	GET /backtests/{id}	$.fulfilled_trades	direct API value (cross-check: count(trades))	68,077
Best trade	GET /backtests/{id}	$.max_profit	direct API value (cross-check: max(trades[].profit))	+0.0175 USDT
Worst trade	GET /backtests/{id}	$.min_profit	direct API value (cross-check: min(trades[].profit))	+0.0050 USDT
Avg profit / trade	GET /backtests/{id}	$.avg_profit	direct API value	+0.0076 USDT
Trades / month	derived	n/a	fulfilled_trades / (durationDays / 30.4375)	5677.0
Max drawdown %	GET /backtests/{id}/equity	$[*].total_value_*	maxDrawdown(equity) - running peak vs. trough	-23.69%
Max drawdown abs	GET /backtests/{id}/equity	$[*].total_value_*	maxDrawdown(equity).abs	-2,370.81 USDT
CAGR %	GET /backtests/{id}/equity	$[*].timestamp, $[*].total_value_*	(final/start)^(365.25/days) - 1	-22.02%
Volatility (ann.) %	GET /backtests/{id}/equity	$[*].timestamp, $[*].total_value_*	std(daily_returns) * sqrt(365)	15.05%
Sharpe ratio (ann.)	GET /backtests/{id}/equity	$[*].timestamp, $[*].total_value_*	mean(daily_returns) / std(daily_returns) * sqrt(365) (rf=0)	-1.58
Sortino ratio (ann.)	GET /backtests/{id}/equity	$[*].timestamp, $[*].total_value_*	mean(daily_returns) / std(downside_returns) * sqrt(365) (rf=0)	-1.45
Time in market %	GET /backtests/{id}/equity	$[*].base_asset_bal	count(samples where base_asset_bal>0) / count(samples) * 100	99.7%
Total fees	GET /backtests/{id}/trades	$[*].buy_fee_in_quote, $[*].sell_fee_in_quote	Sum (buy_fee_in_quote + sell_fee_in_quote)	0.75 USDT
Gross profit (winners)	GET /backtests/{id}/trades	$[?(@.profit>0)].profit	Sum profit where profit > 0	+1.01 USDT
Gross loss (losers)	GET /backtests/{id}/trades	$[?(@.profit<0)].profit	Sum abs(profit) where profit < 0	0.00 USDT
Profit factor	GET /backtests/{id}/trades	$[*].profit	Sum winners.profit / Sum abs(losers.profit)	inf
Payoff ratio	GET /backtests/{id}/trades	$[*].profit	avg(winners.profit) / avg(abs(losers.profit))	inf
Expectancy / trade	GET /backtests/{id}/trades	$[*].profit	mean(trades[].profit)	+0.0101 USDT
Max consecutive wins	GET /backtests/{id}/trades	$[*].profit (ordered)	longest run where profit > 0	100
Max consecutive losses	GET /backtests/{id}/trades	$[*].profit (ordered)	longest run where profit < 0	0
Avg holding (h)	GET /backtests/{id}/trades	$[*].buy_time, $[*].sell_time	mean(sell_time - buy_time) / 3600	3.6h
Median holding (h)	GET /backtests/{id}/trades	$[*].buy_time, $[*].sell_time	median(sell_time - buy_time) / 3600	0.1h
Total volume (notional)	GET /backtests/{id}/trades	$[*].buy_quantity, $[*].buy_price	Sum (buy_quantity * buy_price)	1,006 USDT

Any number in the rest of this document that doesn’t appear here is derived from the rows above via a transform documented inline at its point of use.

Raw API Attachments

These blocks are the unmodified API responses that produced every metric in this report. They are large by design - collapse them with the accordions below if you’re reading the doc as prose. Verify their integrity via the fingerprints in the Data Integrity chunk.

Rendered trade sample: 100-row slice from /trades. Full trade population: 68,077 closed trades - fetch the complete list via GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c/trades. Aggregate KPIs are computed server-side against the full population, not from this sample.

GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c - backtest summary (verbatim)

backtest.json

{
  "id": "14c346cf-9049-46a6-ac75-e17a2e31b31c",
  "status": "completed",
  "error_message": null,
  "created_at": "2026-04-19T17:13:49.783266Z",
  "started_at": "2026-04-20T02:51:43.523947Z",
  "completed_at": "2026-04-20T03:04:20.870961Z",
  "mode_name": "3LongTimeLong.json",
  "symbol": "AGLDUSDT",
  "data_file": "",
  "start_balance": 10000,
  "final_value": 7805.0678681,
  "return_pct": -21.9493,
  "total_profit": 516.31401182,
  "fulfilled_trades": 68077,
  "active_orders": 736,
  "win_rate": 100,
  "avg_profit": 0.00758427,
  "max_profit": 0.0174625,
  "min_profit": 0.00499063,
  "elapsed_sec": 584.63,
  "max_drawdown": -23.6869,
  "profit_factor": null,
  "sharpe_ratio": -1.5763,
  "total_fees": 516.44478983,
  "avg_hold_time_hours": 7.6511,
  "tp_count": 68077,
  "sl_count": 0,
  "tsl_count": 0,
  "start_price": 2.583,
  "end_price": 0.262,
  "gross_profit_quote": 1032.758802,
  "gross_loss_quote": 0,
  "wins": 68077,
  "losses": 0,
  "breakeven": 0,
  "consecutive_wins_max": 68077,
  "consecutive_losses_max": 0,
  "holding_minutes_p50": 8,
  "holding_minutes_p95": 993,
  "holding_minutes_max": 131584,
  "description": "AGLDUSDT | 3LongTimeLong.json | 2025-01-01 - 2025-12-31 | -21.95% | 68077 trades | 100% WR",
  "summary_text": "Backtest AGLDUSDT (Mode: 3LongTimeLong.json)\nPeriod: 2025-01-01 00:00:01 to 2025-12-31 23:59:59\nStarting balance: 10,000.00 USDT\nFinal value: 7,805.07 USDT\nP&L: -2,194.93 USDT (-21.95%)\nResult: LOSS\nCompleted trades: 68077\nOpen orders at end: 736\nWin rate: 100.0%\nAvg. profit/trade: 0.007584 USDT\nBest trade: 0.017462 USDT\nWorst trade: 0.004991 USDT\nTotal profit (trades only): 516.314012 USDT\n\nStrategy parameters:\n  Buy trigger: -0.1% from last buy\n  Buy splits: 9\n  Sell targets: [0.25, 0.35, 0.5, 0.75, 1.0, 2.5, 5.0, 10.0, 15.0]\n  Investment per buy: 25.0 USDT\n  Fees: maker 7.5 bps / taker 7.5 bps\n\nElapsed: 584.6s",
  "share_token": null,
  "config_hash": "123a78aebe26013dd4c61386184430610fcdb8e0ed738704688e293861072e9f",
  "config": {
    "to": "2025-12-31 23:59:59",
    "from": "2025-01-01 00:00:01",
    "canBuy": true,
    "symbol": "AGLDUSDT",
    "canSell": true,
    "canBuyUp": true,
    "startBal": 10000,
    "stepSize": 0.1,
    "stopLoss": false,
    "tickSize": 0.001,
    "buySplits": 9,
    "buyVolumes": [
      20,
      15,
      10,
      10,
      10,
      10,
      5,
      5,
      5
    ],
    "canBuyDown": false,
    "minNotional": 5,
    "buyPercentage": 0.1,
    "fees_in_quote": true,
    "intrabar_mode": "OLHC",
    "maker_fee_bps": 7.5,
    "taker_fee_bps": 7.5,
    "sellPercentages": [
      0.25,
      0.35,
      0.5,
      0.75,
      1,
      2.5,
      5,
      10,
      15
    ],
    "triggerCoolDown": 1,
    "investmentPerBuy": 25,
    "assumed_spread_bps": 0,
    "stopLossPercentage": 5,
    "investmentPercentMode": false,
    "minInvestmentPerQuote": 25,
    "order_latency_seconds": 2,
    "trailingStopLossPercentages": [
      0,
      0,
      0,
      0,
      0,
      0,
      0,
      0,
      0
    ],
    "sellCancelDistancePercentage": 1,
    "dontBuyBelowQuoteAssetBalance": 1,
    "investmentPerFreeQuotePercent": 0.01,
    "sellActivateDistancePercentage": 0.1
  },
  "is_duplicate": false,
  "compact": false
}

GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c/equity - equity series (verbatim, 365 points)

equity.json

[
  {
    "timestamp": "2025-01-01T23:59:00+00:00",
    "base_asset_bal": 0,
    "quote_asset_bal": 10002.7783156,
    "total_value_mid": 10002.7783156,
    "total_value_exit_net": 10002.7783156,
    "total_value": 10002.7783156,
    "base_cost_quote": 0,
    "unrealized_pnl_exit_net": 0
  },
  {
    "timestamp": "2025-01-02T23:59:00+00:00",
    "base_asset_bal": 19.4,
    "quote_asset_bal": 9955.7621552251,
    "total_value_mid": 10006.3573552251,
    "total_value_exit_net": 10006.3194088251,
    "total_value": 10006.3573552251,
    "base_cost_quote": 51.058265,
    "unrealized_pnl_exit_net": -0.5010114
  },
  {
    "timestamp": "2025-01-03T23:59:00+00:00",
    "base_asset_bal": 32,
    "quote_asset_bal": 9917.2869717001,
    "total_value_mid": 10008.9989717001,
    "total_value_exit_net": 10008.9301877001,
    "total_value": 10008.9989717001,
    "base_cost_quote": 93.327042825,
    "unrealized_pnl_exit_net": -1.683826825
  },
  {
    "timestamp": "2025-01-04T23:59:00+00:00",
    "base_asset_bal": 65.3,
    "quote_asset_bal": 9827.1894512001,
    "total_value_mid": 10006.3726512001,
    "total_value_exit_net": 10006.2382638001,
    "total_value": 10006.3726512001,
    "base_cost_quote": 185.204499275,
    "unrealized_pnl_exit_net": -6.155686675
  },
  {
    "timestamp": "2025-01-05T23:59:00+00:00",
    "base_asset_bal": 55.3,
    "quote_asset_bal": 9855.6001014001,
    "total_value_mid": 10008.0622014001,
    "total_value_exit_net": 10007.9478548251,
    "total_value": 10008.0622014001,
    "base_cost_quote": 159.367736225,
    "unrealized_pnl_exit_net": -7.0199828
  },
  {
    "timestamp": "2025-01-06T23:59:00+00:00",
    "base_asset_bal": 80,
    "quote_asset_bal": 9790.3627247001,
    "total_value_mid": 10002.8427247001,
    "total_value_exit_net": 10002.6833647001,
    "total_value": 10002.8427247001,
    "base_cost_quote": 226.043105275,
    "unrealized_pnl_exit_net": -13.722465275
  },
  {
    "timestamp": "2025-01-07T23:59:00+00:00",
    "base_asset_bal": 177.1,
    "quote_asset_bal": 9545.3273947251,
    "total_value_mid": 9976.0345947251,
    "total_value_exit_net": 9975.7115643251,
    "total_value": 9976.0345947251,
    "base_cost_quote": 472.831858125,
    "unrealized_pnl_exit_net": -42.447688525
  },
  {
    "timestamp": "2025-01-08T23:59:00+00:00",
    "base_asset_bal": 250.1,
    "quote_asset_bal": 9378.9086031751,
    "total_value_mid": 9936.1314031751,
    "total_value_exit_net": 9935.7134860751,
    "total_value": 9936.1314031751,
    "base_cost_quote": 641.43571625,
    "unrealized_pnl_exit_net": -84.63083335
  },
  {
    "timestamp": "2025-01-09T23:59:00+00:00",
    "base_asset_bal": 341.1,
    "quote_asset_bal": 9192.5536789501,
    "total_value_mid": 9870.3193789501,
    "total_value_exit_net": 9869.8110546751,
    "total_value": 9870.3193789501,
    "base_cost_quote": 830.406438075,
    "unrealized_pnl_exit_net": -153.14906235
  },
  {
    "timestamp": "2025-01-10T23:59:00+00:00",
    "base_asset_bal": 380.9,
    "quote_asset_bal": 9118.932677975,
    "total_value_mid": 9853.688777975,
    "total_value_exit_net": 9853.1377109,
    "total_value": 9853.688777975,
    "base_cost_quote": 906.387281,
    "unrealized_pnl_exit_net": -172.182248075
  },
  {
    "timestamp": "2025-01-11T23:59:00+00:00",
    "base_asset_bal": 214.9,
    "quote_asset_bal": 9474.791191125,
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]

Verbatim trades.json payload is intentionally not embedded in this report (deprecated). The CSV attachment below is a 100-row audit sample of the full ledger, not the complete trade list - fetch the canonical full population (68,077 closed trades) via GET /backtests/14c346cf-9049-46a6-ac75-e17a2e31b31c/trades (paginate through all pages).

100-row audit sample from 68,077 closed trades (RFC 4180 CSV)

trades.csv

id,symbol,buy_time,sell_time,buy_price,sell_price,buy_quantity,sell_quantity,quantity,profit,profit_percentage,buy_fee_in_quote,sell_fee_in_quote,fee_buy,fee_sell,fill_type
,,2025-01-01T00:01:00Z,2025-01-01T00:10:00Z,2.427,2.434,2.1,,,0.007043925,0.1382056036,0.003822525,0.00383355,,,TP
,,2025-01-01T00:03:00Z,2025-01-01T00:10:00Z,2.414,2.421,2.1,,,0.007084875,0.1397576636,0.00380205,0.003813075,,,TP
,,2025-01-01T00:05:00Z,2025-01-01T00:05:00Z,2.399,2.405,2.1,,,0.0050337,0.0999166319,0.003778425,0.003787875,,,TP
,,2025-01-01T00:06:00Z,2025-01-01T00:06:00Z,2.396,2.402,2.1,,,0.00504315,0.1002295492,0.0037737,0.00378315,,,TP
,,2025-01-01T00:07:00Z,2025-01-01T00:07:00Z,2.39,2.396,2.1,,,0.00506205,0.1008577406,0.00376425,0.0037737,,,TP
,,2025-01-01T00:07:00Z,2025-01-01T00:08:00Z,2.394,2.4,2.1,,,0.00504945,0.1004385965,0.00377055,0.00378,,,TP
,,2025-01-01T00:09:00Z,2025-01-01T00:09:00Z,2.392,2.398,2.1,,,0.00505575,0.1006479933,0.0037674,0.00377685,,,TP
,,2025-01-01T00:11:00Z,2025-01-01T00:14:00Z,2.422,2.429,2.1,,,0.007059675,0.138800578,0.00381465,0.003825675,,,TP
,,2025-01-01T00:15:00Z,2025-01-01T00:15:00Z,2.427,2.434,2.1,,,0.007043925,0.1382056036,0.003822525,0.00383355,,,TP
,,2025-01-01T00:16:00Z,2025-01-01T00:28:00Z,2.429,2.436,2.1,,,0.007037625,0.1379682997,0.003825675,0.0038367,,,TP
,,2025-01-01T00:17:00Z,2025-01-01T00:28:00Z,2.422,2.429,2.1,,,0.007059675,0.138800578,0.00381465,0.003825675,,,TP
,,2025-01-01T00:17:00Z,2025-01-01T00:27:00Z,2.419,2.426,2.1,,,0.007069125,0.1391587433,0.003809925,0.00382095,,,TP
,,2025-01-01T00:19:00Z,2025-01-01T00:25:00Z,2.408,2.415,2.1,,,0.007103775,0.1404796512,0.0037926,0.003803625,,,TP
,,2025-01-01T00:20:00Z,2025-01-01T00:24:00Z,2.404,2.411,2.1,,,0.007116375,0.1409629784,0.0037863,0.003797325,,,TP
,,2025-01-01T00:22:00Z,2025-01-01T00:24:00Z,2.397,2.403,2.1,,,0.00504,0.1001251564,0.003775275,0.003784725,,,TP
,,2025-01-01T00:22:00Z,2025-01-01T00:23:00Z,2.392,2.398,2.1,,,0.00505575,0.1006479933,0.0037674,0.00377685,,,TP
,,2025-01-01T00:23:00Z,2025-01-01T00:24:00Z,2.392,2.398,2.1,,,0.00505575,0.1006479933,0.0037674,0.00377685,,,TP
,,2025-01-01T00:26:00Z,2025-01-01T00:26:00Z,2.413,2.42,2.1,,,0.007088025,0.1398777455,0.003800475,0.0038115,,,TP
,,2025-01-01T00:29:00Z,2025-01-01T00:52:00Z,2.432,2.439,2.1,,,0.007028175,0.1376130757,0.0038304,0.003841425,,,TP
,,2025-01-01T00:31:00Z,2025-01-01T00:31:00Z,2.406,2.413,2.1,,,0.007110075,0.1407211139,0.00378945,0.003800475,,,TP
,,2025-01-01T00:33:00Z,2025-01-01T00:50:00Z,2.421,2.428,2.1,,,0.007062825,0.1389198678,0.003813075,0.0038241,,,TP
,,2025-01-01T00:34:00Z,2025-01-01T00:38:00Z,2.41,2.417,2.1,,,0.007097475,0.1402385892,0.00379575,0.003806775,,,TP
,,2025-01-01T00:36:00Z,2025-01-01T00:38:00Z,2.405,2.412,2.1,,,0.007113225,0.1408419958,0.003787875,0.0037989,,,TP
,,2025-01-01T00:39:00Z,2025-01-01T00:50:00Z,2.415,2.422,2.1,,,0.007081725,0.1396376812,0.003803625,0.00381465,,,TP
,,2025-01-01T00:41:00Z,2025-01-01T00:43:00Z,2.404,2.411,2.1,,,0.007116375,0.1409629784,0.0037863,0.003797325,,,TP
,,2025-01-15T21:29:00Z,2025-01-15T22:54:00Z,2,2.005,2.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-01-15T23:02:00Z,2025-01-15T23:04:00Z,2,2.005,2.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-01-15T23:07:00Z,2025-01-15T23:38:00Z,2,2.005,2.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-01-16T12:49:00Z,2025-01-16T12:50:00Z,2,2.005,2.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-01-16T12:56:00Z,2025-01-16T12:56:00Z,2,2.005,2.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-10T21:43:00Z,2025-10-10T21:44:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-11T01:37:00Z,2025-10-11T02:01:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-11T03:20:00Z,2025-10-11T03:21:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-11T03:34:00Z,2025-10-11T03:35:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-11T03:37:00Z,2025-10-11T03:39:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-11T19:34:00Z,2025-10-11T19:37:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-11T19:38:00Z,2025-10-12T07:49:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-11T19:47:00Z,2025-10-11T19:47:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-11T19:47:00Z,2025-10-11T19:51:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-12T07:49:00Z,2025-10-12T08:02:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-17T06:32:00Z,2025-10-17T06:32:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-17T06:35:00Z,2025-10-17T21:07:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-17T20:41:00Z,2025-10-17T20:43:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-17T20:44:00Z,2025-10-17T20:46:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-17T22:22:00Z,2025-10-18T01:10:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-18T14:10:00Z,2025-10-18T14:18:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-19T00:18:00Z,2025-10-19T01:42:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-19T02:59:00Z,2025-10-19T03:19:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-19T04:29:00Z,2025-10-19T04:33:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-19T05:04:00Z,2025-10-19T05:17:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-19T05:17:00Z,2025-10-19T05:21:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-19T08:11:00Z,2025-10-19T08:18:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-19T08:22:00Z,2025-10-19T09:42:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-21T04:30:00Z,2025-10-21T10:36:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-22T05:26:00Z,2025-10-22T05:28:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-22T11:03:00Z,2025-10-22T11:03:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-22T11:09:00Z,2025-10-22T11:10:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-22T17:45:00Z,2025-10-22T18:05:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-22T18:52:00Z,2025-10-22T18:54:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-22T19:43:00Z,2025-10-22T19:50:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-22T19:50:00Z,2025-10-22T20:02:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-22T20:02:00Z,2025-10-23T00:34:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-23T00:42:00Z,2025-10-23T00:43:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-23T03:16:00Z,2025-10-23T03:21:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-24T16:41:00Z,2025-10-24T16:42:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-24T16:42:00Z,2025-10-24T16:43:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-29T00:09:00Z,2025-10-29T00:10:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-29T11:31:00Z,2025-10-29T13:02:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-29T13:35:00Z,2025-10-29T18:01:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-29T17:46:00Z,2025-10-29T17:52:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-29T17:57:00Z,2025-10-29T17:58:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-29T18:07:00Z,2025-10-29T18:08:00Z,0.4,0.401,12.5,,,0.004990625,0.0998125,0.00375,0.003759375,,,TP
,,2025-10-29T19:15:00Z,2025-10-29T20:00:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-29T20:02:00Z,2025-10-29T20:30:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-10-29T21:03:00Z,2025-11-08T16:10:00Z,0.401,0.403,12.5,,,0.0174625,0.3483790524,0.003759375,0.003778125,,,TP
,,2025-12-31T14:12:00Z,2025-12-31T20:28:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T19:55:00Z,2025-12-31T20:17:00Z,0.261,0.262,19.2,,,0.0116688,0.2328544061,0.0037584,0.0037728,,,TP
,,2025-12-31T20:07:00Z,2025-12-31T20:14:00Z,0.26,0.261,19.3,,,0.011758525,0.2343269231,0.0037635,0.003777975,,,TP
,,2025-12-31T20:20:00Z,2025-12-31T20:25:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T20:28:00Z,2025-12-31T20:29:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T20:29:00Z,2025-12-31T20:32:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T20:33:00Z,2025-12-31T21:06:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T20:45:00Z,2025-12-31T20:45:00Z,0.261,0.262,19.2,,,0.0116688,0.2328544061,0.0037584,0.0037728,,,TP
,,2025-12-31T20:49:00Z,2025-12-31T20:49:00Z,0.261,0.262,19.2,,,0.0116688,0.2328544061,0.0037584,0.0037728,,,TP
,,2025-12-31T20:50:00Z,2025-12-31T21:04:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T21:04:00Z,2025-12-31T21:05:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T21:06:00Z,2025-12-31T21:19:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T21:08:00Z,2025-12-31T21:10:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T21:13:00Z,2025-12-31T21:14:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T21:14:00Z,2025-12-31T21:15:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T21:20:00Z,2025-12-31T21:20:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T21:23:00Z,2025-12-31T21:23:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T21:30:00Z,2025-12-31T21:57:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T21:30:00Z,2025-12-31T21:30:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T21:57:00Z,2025-12-31T22:21:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T22:04:00Z,2025-12-31T22:05:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP
,,2025-12-31T22:23:00Z,2025-12-31T22:25:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T22:48:00Z,2025-12-31T23:20:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T22:48:00Z,2025-12-31T22:48:00Z,0.263,0.264,19.1,,,0.011550725,0.2299429658,0.003767475,0.0037818,,,TP
,,2025-12-31T23:57:00Z,2025-12-31T23:58:00Z,0.262,0.263,19.1,,,0.011579375,0.2313931298,0.00375315,0.003767475,,,TP

Frequently Asked Questions

Deterministic Q&A - every question is always present so this section can be sliced into stable RAG chunks. Missing API fields render as _null_; metrics that can’t be derived locally render as “not available”.

What strategy was tested and on which trading pair?

The LongTimeLong strategy was tested on AGLDUSDT over 2025-01-01 to 2025-12-31.

_{Source: bt.mode_name, bt.symbol, bt.config.from, bt.config.to}

What is the current status of this backtest?

Completed. Final metrics are available below.

_{Source: bt.status, bt.error_message}

What was the total return?

-21.95% - turning 10,000.00 USDT into 7,805.07 USDT (net profit 516.31 USDT).

_{Source: bt.return_pct, bt.start_balance, bt.final_value, bt.total_profit}

What was the annualized growth rate (CAGR)?

Compound Annual Growth Rate is -22.02% over a 364 day window.

_{Source: derived from equity series (eqStats.cagrPct, eqStats.durationDays)}

Did it beat Buy & Hold of AGLDUSDT?

Buy & Hold comparison is not available - token return for the window was not provided.

_{Source: bt.return_pct, opts.tokenReturnPct, outperformDelta}

What was the worst drawdown?

The maximum peak-to-trough loss was -23.69% (2,370.81 USDT), lasting around 349 days underwater and did not recover within the window.

_{Source: derived from equity series (eqStats.maxDrawdownPct, .maxDrawdownAbs, .underwaterDays, .recoveryDays)}

How many trades did it take and what was the win rate?

68,077 trades were filled with a 100.0% win rate (~5677.0 trades per month). 736 positions were still open at the end of the window.

_{Source: bt.fulfilled_trades, bt.win_rate, bt.active_orders, derived tradesPerMonth}

What was the average, best, and worst trade?

Average trade profit was 0.01 USDT, the best single trade made 0.02 USDT, the worst single trade still finished positive at +0.00 USDT (no closed trade ended in a loss).

_{Source: bt.avg_profit, bt.max_profit, bt.min_profit}

What was the profit factor and expectancy?

Profit factor Infinity (gross profit / gross loss) and expectancy 0.01 USDT per trade. Payoff ratio (avg win / avg loss) is Infinity.

_{Source: derived from trades (stats.profitFactor, .expectancy, .payoffRatio)}

What were the longest winning and losing streaks?

Longest winning streak: 100 trades. Longest losing streak: 0 trades.

_{Source: derived from trades (stats.consecWinsMax, .consecLossesMax)}

How much was paid in fees?

Total fees were 0.75 USDT (buy: 0.38, sell: 0.38) over 100 trades. Computed as Sum(buy_fee_in_quote + sell_fee_in_quote) over every row of GET /backtests/{id}/trades - the API summary field bt.total_fees is used only for cross-checking, not as the source of truth.

_{Source: derived from trades[].buy_fee_in_quote + trades[].sell_fee_in_quote (analyzeTrades -> stats.totalFees / .buyFees / .sellFees)}

Is the risk-adjusted return any good?

Annualized Sharpe is -1.58 and Sortino is -1.45. Sharpe > 1 is generally considered good, > 2 very good, > 3 excellent (caveat: crypto Sharpe is easily inflated by short windows).

_{Source: derived from equity series (eqStats.sharpeAnnualized, .sortinoAnnualized)}

How volatile was the equity curve?

Annualized volatility of daily returns was 15.05%.

_{Source: derived from equity series (eqStats.volatilityAnnualizedPct)}

How long does the strategy hold positions?

The median hold time is 6.0 minutes (mean 3.6 hours, range 1.0 minutes - 9.8 days).

_{Source: derived from trades (stats.medianHoldingHours, .avgHoldingHours, .minHoldingHours, .maxHoldingHours)}

How much of the time was capital deployed?

The strategy was in the market 99.7% of the period - the remainder sat in cash awaiting setups.

_{Source: derived from equity series (eqStats.timeInMarketPct)}

What were the best and worst months?

Best month: 2025-10 with 0.52 USDT. Worst month: 2025-11 with 0.02 USDT.

_{Source: derived from trades (stats.bestMonth, .worstMonth)}

When did the backtest run and how long did it take?

Created at 2026-04-19T17:13:49.783266Z, started at 2026-04-20T02:51:43.523947Z, completed at 2026-04-20T03:04:20.870961Z. Engine elapsed time: 584.63 s.

_{Source: bt.created_at, bt.started_at, bt.completed_at, bt.elapsed_sec}

What configuration parameters were used?

Initial capital 10,000.00 USDT, data file null, timeframe null, fee % null, cloned from a base mode: no.

_{Source: bt.start_balance, bt.data_file, bt.config.timeframe, bt.config.fee_pct, bt.is_duplicate}

Where can I find a one-line summary written by the engine?

Engine summary: Backtest AGLDUSDT (Mode: 3LongTimeLong.json)
Period: 2025-01-01 00:00:01 to 2025-12-31 23:59:59
Starting balance: 10,000.00 USDT
Final value: 7,805.07 USDT
P&L: -2,194.93 USDT (-21.95%)
Result: LOSS
Completed trades: 68077
Open orders at end: 736
Win rate: 100.0%
Avg. profit/trade: 0.007584 USDT
Best trade: 0.017462 USDT
Worst trade: 0.004991 USDT
Total profit (trades only): 516.314012 USDT

Strategy parameters:
  Buy trigger: -0.1% from last buy
  Buy splits: 9
  Sell targets: [0.25, 0.35, 0.5, 0.75, 1.0, 2.5, 5.0, 10.0, 15.0]
  Investment per buy: 25.0 USDT
  Fees: maker 7.5 bps / taker 7.5 bps

Elapsed: 584.6s

_{Source: bt.summary_text}

Are these results guaranteed for live trading?

No. This is a historical backtest. It excludes live slippage variance, exchange outages, latency, and regime changes. Treat it as evidence the rules can be profitable, not a forward guarantee.

_{Source: static disclaimer}

Glossary

Definitions for every metric used in this report. Use these to disambiguate when answering questions.

Term	Definition
Return %	Total percentage change of portfolio value over the backtest window: (final_value - start_balance) / start_balance x 100.
CAGR	Compound Annual Growth Rate. Annualized return assuming compounding: (final/start)^(365.25/days) - 1.
Win Rate	Share of closed trades with positive net P&L (after fees): wins / (wins + losses) x 100.
Max Drawdown	Largest peak-to-trough decline of equity, measured in % of the previous peak. The single most important risk metric.
Drawdown Duration	Number of days spent below the previous equity peak before recovering (or end of window if not recovered).
Sharpe Ratio (annualized)	Mean daily return divided by standard deviation of daily returns, scaled by 365. Risk-adjusted return - higher is better, > 1 is decent.
Sortino Ratio (annualized)	Like Sharpe but only penalizes downside volatility. Often more representative for asymmetric strategies.
Volatility (ann.)	Standard deviation of daily returns x 365, expressed as a percentage.
Time in Market	Fraction of the period where the strategy held base-asset exposure (not 100% cash).
Buy & Hold	Reference benchmark: simply buying the underlying token at the start and holding to the end of the window.
Outperformance (pp)	Strategy return minus Buy & Hold return, in percentage points (not percent change).
Fulfilled Trades	Round-trip trades that have both an entry fill and an exit fill within the window.
Active Orders	Open positions still pending exit at the end of the backtest - their unrealized P&L is included in final_value.
Lookahead Bias	Using information that wouldn’t have been available at trade time. The validator flags trades whose buy_time precedes the configured from date.

How to Read This Report

Data source - GET /backtests/{id} * Fields used: return_pct, fulfilled_trades, win_rate

1. Check the headline return

   The strategy returned -21.95% over the tested window. Compare against Buy & Hold to judge true edge.

2. Validate trade frequency

   68,077 executed trades - statistically meaningful sample size.

3. Inspect the win rate

   Win rate of 100.0% indicates a high-probability setup.

4. Review the equity curve

   Look for steady upward slope vs. spiky behaviour. Smooth growth is more robust to live deployment than lumpy gains.

Reproduce This Run

Data source - GET /backtests/{id} * Fields used: symbol, mode_name, config (replayed verbatim)

The backtester UI lives at uncoded.ch/backtesting. The REST API endpoint below accepts the same JSON payload - authenticate with your X-API-Key. Payload fields are validated against ENGINE.md Section 20; non-engine keys are stripped so the replay produces the exact same run.

cURL

curl -X POST https://uncoded-backtest-api-production.up.railway.app/backtests \
  -H "X-API-Key: $UNCODED_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"symbol":"AGLDUSDT","mode_name":"3LongTimeLong.json","interval":"1m","from":"2025-01-01 00:00:01","to":"2025-12-31 23:59:59","canBuy":true,"canSell":true,"canBuyUp":true,"startBal":10000,"stepSize":0.1,"stopLoss":false,"tickSize":0.001,"buySplits":9,"buyVolumes":[20,15,10,10,10,10,5,5,5],"canBuyDown":false,"minNotional":5,"buyPercentage":0.1,"fees_in_quote":true,"intrabar_mode":"OLHC","maker_fee_bps":7.5,"taker_fee_bps":7.5,"sellPercentages":[0.25,0.35,0.5,0.75,1,2.5,5,10,15],"triggerCoolDown":1,"investmentPerBuy":25,"assumed_spread_bps":0,"stopLossPercentage":5,"investmentPercentMode":false,"minInvestmentPerQuote":25,"order_latency_seconds":2,"trailingStopLossPercentages":[0,0,0,0,0,0,0,0,0],"sellCancelDistancePercentage":1,"dontBuyBelowQuoteAssetBalance":1,"investmentPerFreeQuotePercent":0.01,"sellActivateDistancePercentage":0.1}'

Structured Data (JSON-LD)

Schema.org Dataset markup with 34 PropertyValue KPIs. Every numeric value is finite + range-validated; invalid / null fields are omitted so the JSON stays schema.org-compliant.

schema.org

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "AGLDUSDT LongTimeLong Backtest",
  "alternateName": "AGLDUSDT-3LongTimeLong.json-14c346cf-9049-46a6-ac75-e17a2e31b31c",
  "description": "Backtest of the LongTimeLong strategy on AGLDUSDT: -21.95% return over 2025-01-01 to 2025-12-31.",
  "identifier": "14c346cf-9049-46a6-ac75-e17a2e31b31c",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "isAccessibleForFree": true,
  "inLanguage": "en",
  "creativeWorkStatus": "completed",
  "creator": {
    "@type": "Organization",
    "name": "uncoded.ch",
    "url": "https://uncoded.ch/backtesting"
  },
  "publisher": {
    "@type": "Organization",
    "name": "uncoded.ch",
    "url": "https://uncoded.ch"
  },
  "keywords": [
    "AGLDUSDT",
    "LongTimeLong",
    "crypto backtest",
    "trading strategy",
    "quantitative finance",
    "algorithmic trading"
  ],
  "about": [
    {
      "@type": "Thing",
      "name": "AGLDUSDT",
      "description": "Trading pair: AGLDUSDT"
    },
    {
      "@type": "Thing",
      "name": "LongTimeLong",
      "description": "Trading strategy: LongTimeLong"
    }
  ],
  "measurementTechnique": "Historical OHLCV replay with deterministic order-fill simulation (TP / SL / Trailing-SL)",
  "variableMeasured": [
    {
      "@type": "PropertyValue",
      "name": "Return",
      "value": -21.9493,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Total percentage return over the backtest window"
    },
    {
      "@type": "PropertyValue",
      "name": "Win Rate",
      "value": 100,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Share of closed trades with positive net P&L",
      "minValue": 0,
      "maxValue": 100
    },
    {
      "@type": "PropertyValue",
      "name": "Net Profit",
      "value": 516.31401182,
      "unitText": "USDT",
      "description": "Total realized profit in quote currency"
    },
    {
      "@type": "PropertyValue",
      "name": "Final Portfolio Value",
      "value": 7805.0678681,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Initial Capital",
      "value": 10000,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Fulfilled Trades",
      "value": 68077,
      "unitText": "trades",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Active Orders",
      "value": 736,
      "unitText": "orders",
      "description": "Open positions still pending exit at end of window",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Best Trade Profit",
      "value": 0.0174625,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Worst Trade Profit",
      "value": 0.00499063,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Average Trade Profit",
      "value": 0.00758427,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Engine Elapsed Time",
      "value": 584.63,
      "unitText": "seconds",
      "unitCode": "SEC",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "CAGR",
      "value": -22.015714364737203,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Compound Annual Growth Rate"
    },
    {
      "@type": "PropertyValue",
      "name": "Max Drawdown",
      "value": -23.68693384147357,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Largest peak-to-trough decline of equity (negative)",
      "maxValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Max Drawdown (absolute)",
      "value": -2370.8086717997994,
      "unitText": "USDT",
      "maxValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Drawdown Duration",
      "value": 349,
      "unitText": "days",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Sharpe Ratio (annualized)",
      "value": -1.5763389865112414,
      "description": "Mean / std of daily returns x 365"
    },
    {
      "@type": "PropertyValue",
      "name": "Sortino Ratio (annualized)",
      "value": -1.446884625415156,
      "description": "Like Sharpe but only penalizes downside volatility"
    },
    {
      "@type": "PropertyValue",
      "name": "Volatility (annualized)",
      "value": 15.054449019971974,
      "unitText": "%",
      "unitCode": "P1",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Time in Market",
      "value": 99.72602739726028,
      "unitText": "%",
      "unitCode": "P1",
      "minValue": 0,
      "maxValue": 100
    },
    {
      "@type": "PropertyValue",
      "name": "Backtest Duration",
      "value": 364,
      "unitText": "days",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Equity Samples",
      "value": 365,
      "unitText": "points",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Expectancy per Trade",
      "value": 0.010116286,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Average Win",
      "value": 0.010116286,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Average Loss",
      "value": 0,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Median Trade Profit",
      "value": 0.00933355,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Gross Profit",
      "value": 1.0116286,
      "unitText": "USDT",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Gross Loss",
      "value": 0,
      "unitText": "USDT",
      "maxValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Total Fees Paid",
      "value": 0.7545713999999991,
      "unitText": "USDT",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Total Volume Traded",
      "value": 1006.0952000000003,
      "unitText": "USDT",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Longest Winning Streak",
      "value": 100,
      "unitText": "trades",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Longest Losing Streak",
      "value": 0,
      "unitText": "trades",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Median Holding Time",
      "value": 0.1,
      "unitText": "hours",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Mean Holding Time",
      "value": 3.632730923694779,
      "unitText": "hours",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Trades per Month",
      "value": 5676.96936688035,
      "unitText": "trades/month",
      "minValue": 0
    }
  ],
  "dateCreated": "2026-04-19T17:13:49.783Z",
  "dateModified": "2026-05-15T12:19:41.652Z",
  "temporalCoverage": "2024-12-31T23:00:01.000Z/2025-12-31T22:59:59.000Z"
}

[!] Disclaimer

Backtest results are based on historical market data and do not guarantee future performance. Past returns are not indicative of future results. Trade at your own risk.

---

Backtester: uncoded.ch/backtesting * Generated 2026-05-15 12:19:41 UTC