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Documentation Index

Fetch the complete documentation index at: https://uncoded.ch/docs/llms.txt

Use this file to discover all available pages before exploring further.

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DYDXUSDT * FullBullMarket

Strong window result: this strategy more than 2x’d the starting capital over the backtest window. Max drawdown reached -36.46%, so the equity path was significant - investors would have sat through a deep drawdown before the recovery.
DYDXUSDT | 1FullBullMarket.json | 2023-01-01 - 2023-12-31 | +107.37% | 90,512 closed trades | 100.0% closed WR | 965 open orders

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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 965 open orders carrying -1,418.41 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

+107.37%

Final Value

20,736.74 USDT

Portfolio PnL

+10,736.74 USDT (realized + unrealized)

Realized Profit

+12,279.13 USDT (closed trades only)

Unrealized PnL

-1,418.41 USDT (open positions, mark-to-market)

Win Rate (closed)

100.0% (excludes 965 open orders)

Closed Trades

90,512

Open Orders

965

Best Closed Trade

+1.4801 USDT

Worst Closed Trade

+0.0099 USDT

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TL;DR

The FullBullMarket strategy on DYDXUSDT returned +107.37% over 2023-01-01 to 2023-12-31, turning 10,000.00 USDT into 20,736.74 USDT. It executed 90,512 closed trades with a 100.0% win rate (closed only), hitting a max drawdown of -36.46%. [!] This 100.0% win rate is a structural artifact, not edge - closed-trade WR excludes open positions by definition, and 965 orders remain open carrying -1,418.41 USDT of unrealized loss. The losing trades are sitting unrealised in the open buy-ladder, not absent. It underperformed Buy & Hold by -60.09% on the same window.

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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

-36.46%

Profit Factor

inf (no losing trades)

Sharpe Ratio

1.47

Total Fees

1,396.54 USDT

Avg Hold Time

91.7 h

TP / SL / TSL

90,512 / 0 / 0

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Trade Economics

MetricValueInterpretation
Profit Factorinf (no losing trades)Sum of winning trades / sum of losing trades. >1 = profitable edge, >2 = strong, inf = no losses.
Gross Profit13,675.67 USDTSum of all winning trades before fees.
Gross Loss0.00 USDTAbsolute sum of all losing trades.
Total Fees1,396.54 USDTBuy + sell fees across all trades.
Fee Drag10.2% of gross profitWhat share of edge the exchange consumed.
Avg Hold Time91.7 hMean duration a position is open.

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Exit Mix (Fill Types)

Exit TypeCountShareWhat It Means
TP (Take Profit)90,512100.0%Position closed by hitting the profit target - the desired outcome.
SL (Stop Loss)00.0%Position closed by hitting the stop - capital preservation in action.
TSL (Trailing Stop)00.0%Trailing stop locked in profit during a trend.

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Verdict

For DYDXUSDT, 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, fee drag is healthy at 10.2% of gross profit.

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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.

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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="f5b83fa5-668f-4498-adf6-94c6c288fa03" */}
...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[^>]*-->.)

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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).

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Chunk type taxonomy

TypePurposeBest for retrieval of…
metaFrontmatter & document introdocument-identity questions
summaryTL;DR-style condensed prose”give me the headline”
kpisHeadline numbers, card gridsnumeric lookups
overviewTabular meta (period, balance, …)“what was tested”
comparisonStrategy vs benchmark”did it beat buy & hold”
claimsAssertion -> evidence -> sourcecitation-grade answers
configReproduction parameters”how do I rerun this”
chartSingle visualization w/ embedded datachart-specific questions
catalogueMulti-chart bundleexploratory chart questions
risk / allocation / trade-stats / monthly / top-tradesDomain-specific analyticstargeted KPI questions
validationData-quality reporttrust / sanity questions
factsMachine-readable JSON + key-valuedeterministic lookups
qaPre-answered FAQdirect Q&A retrieval
glossaryTerm definitionsdisambiguation
narrativeHuman-oriented prose”explain like I’m…”
structuredJSON-LD / schema.orgsearch-engine indexing
provenanceMetric -> API field -> formula lookup”where does this number come from”
integritySHA-256 fingerprints of raw API payloadstamper-evidence checks
attachmentsVerbatim API responses + trade-sample CSV (full ledger via /trades pagination)byte-level reproduction
footerDisclaimers, linkscompliance lookups
  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.

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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 backtest produced a 107.37% return on DYDXUSDT between 2023-01-01 and 2023-12-31. Returns of this magnitude in a single historical window typically reflect a favourable price trajectory of the underlying asset combined with the FullBullMarket configuration’s exit behaviour, and should not be assumed to repeat in other windows or in live trading. About DYDXUSDT: DYDXUSDT 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 90.512 closed trades on DYDXUSDT is unusually high. Strategies that win this often typically use small take-profits relative to stop-losses, which works until a single large adverse DYDXUSDT move erases many small wins. This figure covers closed trades only and excludes 965 orders that were still open at the end of the window. At roughly 248.0 DYDXUSDT 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: 1.4801 USDT. Worst: 0.0099 USDT. Average per trade: 0.1357 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 +0.01%, giving a best-vs-worst ratio of 148.80:1. 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 FullBullMarket strategy: FullBullMarket is tuned for sustained uptrends - it scales position size into rising markets and gives winners room to run. It tends to underperform in choppy or bear conditions. Configuration analysis: The FullBullMarket configuration entered on a 0.1% pullback signal across 6 potential buy splits at 50 USDT each. Total deployable notional is therefore 300 USDT - a position-sizing footprint that is defensive at 3% 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 6 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 107.37% 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 12279.13 USDT of realised trade profit on a 10000 USDT starting balance, ending at a portfolio value of 20736.74 USDT. Mechanically annualising the 107.37% window return projects to roughly +107.4% 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.

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Methodology & data

This backtest was executed on historical Binance Spot 1-minute candles for DYDXUSDT, 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 FullBullMarket 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 DYDXUSDT 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.

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Live trading considerations

Translating this result to live trading: DYDXUSDT 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.

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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”.
Yes - this clears the bar most automated crypto strategies fail to reach over a comparable window.
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.
Compounding the 107.37% over 365 days projects to +107.4% per year. This is a directional indicator only - crypto regimes change, and strategies rarely sustain peak performance year-over-year.
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.
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 FullBullMarket 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 DYDXUSDT.

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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
FieldValue
Trading PairDYDXUSDT
StrategyFullBullMarket
Period2023-01-01 -> 2023-12-31
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 duration364.0 days (1.00 years) - first -> last equity sample timestamp; used for CAGR / Sharpe / Sortino
Start Balance10,000.00 USDT
Final Value20,736.74 USDT
Avg Profit / Trade+0.1357 USDT
Trades / Month7547.8
Statuscompleted
Created2026-04-18 10:03:54 UTC
Started2026-04-19 11:15:50 UTC
Completed2026-04-19 11:56:06 UTC
Compute Time37m 31s
Backtest IDf5b83fa5-668f-4498-adf6-94c6c288fa03
Engine summary:
Backtest DYDXUSDT (Mode: 1FullBullMarket.json)
Period: 2023-01-01 00:00:01 to 2023-12-31 23:59:59
Starting balance: 10,000.00 USDT
Final value: 20,736.74 USDT
P&L: +10,736.74 USDT (+107.37%)
Result: PROFIT
Completed trades: 90512
Open orders at end: 965
Win rate: 100.0%
Avg. profit/trade: 0.135663 USDT
Best trade: 1.480073 USDT
Worst trade: 0.009947 USDT
Total profit (trades only): 12,279.126502 USDT

Strategy parameters:
  Buy trigger: -0.1% from last buy
  Buy splits: 6
  Sell targets: [0.25, 2.5, 5.0, 10.0, 20.0, 30.0]
  Investment per buy: 50.0 USDT
  Fees: maker 7.5 bps / taker 7.5 bps

Elapsed: 2250.6s

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vs Buy & Hold

Data source - GET /backtests/{id} * Fields used: return_pct
Buy & Hold reference computed from the underlying token’s price change over the same window (passed in via tokenReturnPct).
Underperforms Buy & Hold by -60.09% over the same window.
MetricStrategyBuy & HoldDelta
Return+107.37%+167.45%-60.09%

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Verifiable Claims

Each claim is grounded in a specific field + source so an LLM can cite evidence rather than paraphrase.
ClaimEvidenceSource
Strategy returned +107.37% over the tested windowreturn_pct = 107.3674GET /backtests/{id} -> return_pct
Capital grew from 10,000.00 to 20,736.74 USDTfinal_value - start_balance = +10,736.74 USDTGET /backtests/{id} -> start_balance, final_value
Roughly 90512 of 90512 trades were profitablewin_rate = 100%GET /backtests/{id} -> win_rate, fulfilled_trades
Worst peak-to-trough loss was 36.46%max_drawdown_pct = 36.4607derived client-side from /equity series
Risk-adjusted return (Sharpe, annualized, rf=0) = 1.47sharpe_annualized = 1.4705derived from daily-resampled equity returns
Strategy lagged Buy & Hold by -60.09%return_pct - buyhold_return_pct = -60.0850 ppBuy & Hold computed from underlying token price change

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Strategy Configuration

Data source - GET /backtests/{id} * Fields used: mode_name, config (full JSON payload)
Mode FullBullMarket governs how buy triggers, position sizing and exits behave. Below are the exact parameters used for this run.
ParameterValue
canBuytrue
canSelltrue
canBuyUptrue
startBal10000
stepSize0.01
stopLossfalse
tickSize0.00001
buySplits6

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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.
ParameterValueMeaning
assumed_spread_bps0Spread (basis points) added to taker fills on top of the candle price.
buyPercentage0.1Buy-trigger threshold as a fraction (e.g. 0.1 = -0.1% from last reference price).
buySplits6Number of price levels in the buy-ladder (DCA depth).
buyVolumes[20,20,20,30,5,5]Per-split sizing weights (sum ~ 100). Distributes investmentPerBuy x buySplits across the ladder.
canBuytrueMaster switch - disables all buy-side logic when false.
canBuyDownfalseAllow re-entries below the last buy price.
canBuyUptrueAllow re-entries above the last buy price (true) or only below (false).
canSelltrueMaster switch - disables all sell-side logic when false.
dontBuyBelowQuoteAssetBalance1Refuse to open new buys if free quote drops below this absolute USDT amount.
fees_in_quotetrueIf true, fees are deducted from the quote (USDT) leg of every fill.
intrabar_modeOLHCIntra-bar fill model (OLHC, OHLC, …) - see ENGINE.md Section 4.
investmentPerBuy50Notional invested per buy event, in quote (USDT).
investmentPerFreeQuotePercent0.01When investmentPercentMode = true: fraction of free quote balance to commit per buy.
investmentPercentModefalseIf true, sizing is taken as a fraction of free quote balance instead of fixed USDT.
maker_fee_bps7.5Maker fee in basis points (e.g. 7.5 = 0.075%).
minInvestmentPerQuote20Hard floor on per-buy notional, regardless of percent-mode math.
minNotional5Exchange minimum notional per order (USDT).
order_latency_seconds2Latency injected between signal time and order placement.
sellActivateDistancePercentage0.1Distance from buy price (%) at which a TSL becomes armed.
sellCancelDistancePercentage1Distance (%) at which an unfilled sell order is cancelled and re-priced.
sellPercentages[0.25,2.5,5,10,20,30]Take-profit ladder, each value is the per-tier profit target as a fraction.
startBal10000Initial portfolio value in the quote currency (USDT).
stepSize0.01Exchange size step (base asset).
stopLossfalseHard stop-loss enabled flag.
stopLossPercentage5Hard stop-loss distance from entry as a percent (5.0 = -5%).
taker_fee_bps7.5Taker fee in basis points.
tickSize0.00001Exchange price tick (quote currency).
trailingStopLossPercentages[0,0,0,0,0,0]Per-tier trailing-SL distances. Zero entries disable TSL on that tier.
triggerCoolDown1Minimum bars/ticks between two consecutive buys on the same level.

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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 == "1FullBullMarket.json" to inspect the public rule fields, or consult the engine source for the executable logic.

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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).

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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.
FieldValueSource
Market-data providerBinance Spotcaller DataSourceInput.provider
Exchange / venuebinancecaller DataSourceInput.exchange
SymbolDYDXUSDTGET /backtests/{id} -> $.symbol
Candle interval1mplatform invariant - all backtests run on 1-minute OHLCV candles
First candle (UTC)2023-01-01 00:00:01GET /backtests/{id} -> $.config.from
Last candle (UTC)2023-12-31 23:59:59GET /backtests/{id} -> $.config.to
TimezoneUTCdefault assumption (UTC)
Engine versionuncoded-engine (FastAPI backend)caller DataSourceInput.engineVersion
Engine spec version1.1caller DataSourceInput.engineSpecVersion
Engine spec documenthttps://uncoded.ch/docs/enginecaller DataSourceInput.engineSpecPath
Full data-source coverage (10/10 fields populated).

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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 20,736.74 USDT across 365 data points. 
    21,224 |                                                          ##
           |                                                    ######::
           |                                                   #::::::::
           |                                                 ##:::::::::
           |                 #                               :::::::::::
           |                 :###            #  # ###    #  #:::::::::::
           |         #  #####::::#####   #  #:##:#:::####:##::::::::::::
           |    #####:# ::::::::::::::  #:##::::::::::::::::::::::::::::
           |   #:::::::#::::::::::::::##::::::::::::::::::::::::::::::::
    10,051 |###:::::::::::::::::::::::::::::::::::::::::::::::::::::::::
           +------------------------------------------------------------
           start                                                   end

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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

-36.46% (-5,934.91 USDT)

CAGR

+107.89%

Volatility (ann.)

63.35%

Sharpe Ratio

1.47

Sortino Ratio

1.53

Time in Market

100.0%

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Drawdown Detail

FieldValue
Peak16,277.56 USDT @ 2023-04-18 23:59:00 UTC
Trough10,342.65 USDT @ 2023-06-14 23:59:00 UTC
Drawdown Duration57.0 days
Recovery2023-10-24 23:59:00 UTC (132.0 days to recover)
All-time Peak21,765.39 USDT

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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 (2026-04-19 11:56:06 UTC):
AssetQuantityValue (USDT)Allocation
USDT (Cash)11,120.4411,120.4453.6%
DYDX3,262.210000000.000.0%
Other (open positions, fee reserves, …)-9,616.3046.4%
Total-20,736.74100.0%
Unrealized P&L on remaining position: -1,418.41 USDT across 965 open order(s). The “Other” row, when present, is dominated by these open positions held outside the cash/base buckets.

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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

+0.5052 USDT

Avg Win

+0.5052 USDT

Avg Loss

-0.0000 USDT

Median Trade

+0.0100 USDT

Max Win Streak

100

Max Loss Streak

0

Avg Holding

679.9h

Median Holding

12.7h

Total Volume

2,365 USDT

Total Fees

1,396.54 USDT * engine-tracked, includes open-position buy fees

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Gross Performance

MetricValueSource
Gross Profit (winners only)+50.52 USDTSum profit where profit > 0 (client-side from /trades)
Gross Loss (losers only)-0.00 USDTSum -profit where profit < 0 (client-side from /trades)
Net Profit+50.52 USDTGross Profit - Gross Loss
Closed-trade buy fees0.87 USDTSum buy_fee_in_quote over matched-pair rows of /trades
Closed-trade sell fees0.91 USDTSum sell_fee_in_quote over matched-pair rows of /trades
Closed-trade fees subtotal1.77 USDTbuy + sell on matched pairs
Open-position buy fees1,394.77 USDTbt.total_fees - closed-trade subtotal - buy-side fees on the 965 orders still open at end of window (no matching sell row yet)
Total Fees Paid (authoritative)1,396.54 USDTbt.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 Profit2764.52%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.
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 itemFormulaAPI field(s)Endpoint
Closed-trade buy feesSum row.buy_fee_in_quotebuy_fee_in_quote per rowGET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03/trades
Closed-trade sell feesSum row.sell_fee_in_quotesell_fee_in_quote per rowGET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03/trades
(A) Closed-trade subtotalSum (buy_fee_in_quote + sell_fee_in_quote)both fee fields, matched-pair rows onlyGET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03/trades
(B) Total Fees Paid (engine)direct readtotal_fees (scalar)GET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03
(C) = (B) - (A) Open-position buy feesbt.total_fees - closed_subtotalderived-
Open orders countdirect readactive_ordersGET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03
Numerical walk-through for this run:
(A) Closed-trade subtotal     = 0.867347  +  0.905958
                              = 1.773305 USDT
(B) bt.total_fees             = 1,396.542348 USDT     <- authoritative
(C) Open-position buy fees    = (B) - (A)
                              = 1,396.542348 - 1.773305
                              = 1,394.769043 USDT
Identity check:  (A) + (C) ?= (B)
                 1.773305 + 1,394.769043 = 1,396.542348 USDT
                 vs bt.total_fees = 1,396.542348 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 965 open orders.

​
Holding Period Distribution

MinMedianAvgMax
0.0h12.7h679.9h6867.5h

​
Trade Timeline

First TradeLast Trade
2023-01-01 00:04:00 UTC2023-12-31 23:50:00 UTC

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Breakdown by Exit Type

TypeCountCumulative ProfitAvg Return
TP100+50.52 USDT+3.52%

​
Monthly Performance

Data source - GET /backtests/{id}/trades * Fields used: sell_time (bucket key), profit
Realized P&L bucketed by sell-time month (7 active months).
Best month: 2023-11 with +35.62 USDT
MonthTradesWinsWin RateNet P&L (USDT)
2023-012626100.0%+13.04
2023-0222100.0%+1.49
2023-0366100.0%+0.06
2023-0533100.0%+0.03
2023-1033100.0%+0.03
2023-113535100.0%+35.62
2023-122525100.0%+0.25

​
Top Trades

Data source - GET /backtests/{id}/trades * Fields used: fill_type, buy_time, sell_time, buy_price, sell_price, profit, profit_percentage
Showing top 10 of 100 profitable trades.
#TypePeriodBuySellProfit%
1TP2023-11-15 -> 2023-11-153.7877804.166560+1.4801+9.84%
2TP2023-11-15 -> 2023-11-153.9159104.307510+1.4801+9.84%
3TP2023-11-14 -> 2023-11-153.7036904.074060+1.4800+9.84%
4TP2023-11-14 -> 2023-11-153.8358304.219420+1.4800+9.84%
5TP2023-11-15 -> 2023-11-153.9569504.352650+1.4800+9.84%
6TP2023-02-02 -> 2023-11-143.5375303.891290+1.4798+9.84%
7TP2023-11-15 -> 2023-11-153.9459404.340540+1.4797+9.84%
8TP2023-11-17 -> 2023-11-243.3783703.716210+1.4797+9.84%
9TP2023-11-14 -> 2023-11-153.8448404.229330+1.4797+9.84%
10TP2023-02-02 -> 2023-11-153.6136003.974970+1.4796+9.84%
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.

​
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).
StatValue
Shallowest DD0.00% @ 2023-01-01 23:59:00 UTC
Deepest DD-36.46% @ 2023-06-14 23:59:00 UTC
Mean-9.18%
Std Dev7.98%
Last-4.73%
Points365
Across 365 points the series ranged from -36.46% (2023-06-14 23:59:00 UTC) to 0.00% (2023-01-01 23:59:00 UTC), averaging -9.18%. 0% of points were positive, 87% negative. The most recent value is -4.73%.
Showing 60 of 365 points (down-sampled for readability).
Drawdown
[{"timestamp":"2023-01-01T23:59:00+00:00","drawdown":0},{"timestamp":"2023-01-07T23:59:00+00:00","drawdown":0},{"timestamp":"2023-01-13T23:59:00+00:00","drawdown":0},{"timestamp":"2023-01-20T23:59:00+00:00","drawdown":0},{"timestamp":"2023-01-26T23:59:00+00:00","drawdown":0},{"timestamp":"2023-02-01T23:59:00+00:00","drawdown":0},{"timestamp":"2023-02-07T23:59:00+00:00","drawdown":0},{"timestamp":"2023-02-13T23:59:00+00:00","drawdown":-12.796639677472374},{"timestamp":"2023-02-19T23:59:00+00:00","drawdown":-4.385881947053835},{"timestamp":"2023-02-26T23:59:00+00:00","drawdown":-1.8039610001360835},{"timestamp":"2023-03-04T23:59:00+00:00","drawdown":-12.794838371081957},{"timestamp":"2023-03-10T23:59:00+00:00","drawdown":-24.566945056111223},{"timestamp":"2023-03-16T23:59:00+00:00","drawdown":-8.935062252137792},{"timestamp":"2023-03-22T23:59:00+00:00","drawdown":-4.5631168944354705},{"timestamp":"2023-03-28T23:59:00+00:00","drawdown":-2.396766493657639},{"timestamp":"2023-04-04T23:59:00+00:00","drawdown":-2.3409486057271116},{"timestamp":"2023-04-10T23:59:00+00:00","drawdown":-1.422855207583479},{"timestamp":"2023-04-16T23:59:00+00:00","drawdown":-0.23720260402282745},{"timestamp":"2023-04-22T23:59:00+00:00","drawdown":-7.831528298399129},{"timestamp":"2023-04-28T23:59:00+00:00","drawdown":-3.3258515865918743},{"timestamp":"2023-05-04T23:59:00+00:00","drawdown":-7.4936855419372},{"timestamp":"2023-05-11T23:59:00+00:00","drawdown":-21.251475534963618},{"timestamp":"2023-05-17T23:59:00+00:00","drawdown":-12.943631446148213},{"timestamp":"2023-05-23T23:59:00+00:00","drawdown":-15.17607539580236},{"timestamp":"2023-05-29T23:59:00+00:00","drawdown":-18.462677491357933},{"timestamp":"2023-06-04T23:59:00+00:00","drawdown":-14.997154977298266},{"timestamp":"2023-06-10T23:59:00+00:00","drawdown":-34.944381317434214},{"timestamp":"2023-06-17T23:59:00+00:00","drawdown":-30.39895139216162},{"timestamp":"2023-06-23T23:59:00+00:00","drawdown":-22.14757245195682},{"timestamp":"2023-06-29T23:59:00+00:00","drawdown":-18.16608698638149},{"timestamp":"2023-07-05T23:59:00+00:00","drawdown":-20.70630427684996},{"timestamp":"2023-07-11T23:59:00+00:00","drawdown":-21.79964288248043},{"timestamp":"2023-07-17T23:59:00+00:00","drawdown":-10.78030766049321},{"timestamp":"2023-07-24T23:59:00+00:00","drawdown":-12.338146762634063},{"timestamp":"2023-07-30T23:59:00+00:00","drawdown":-13.2315435404392},{"timestamp":"2023-08-05T23:59:00+00:00","drawdown":-12.035938881722492},{"timestamp":"2023-08-11T23:59:00+00:00","drawdown":-8.171833605130104},{"timestamp":"2023-08-17T23:59:00+00:00","drawdown":-17.947258669892165},{"timestamp":"2023-08-23T23:59:00+00:00","drawdown":-7.293075866621226},{"timestamp":"2023-08-30T23:59:00+00:00","drawdown":-7.221305278367749},{"timestamp":"2023-09-05T23:59:00+00:00","drawdown":-7.488793237276595},{"timestamp":"2023-09-11T23:59:00+00:00","drawdown":-16.895128975666086},{"timestamp":"2023-09-17T23:59:00+00:00","drawdown":-14.164550089280043},{"timestamp":"2023-09-23T23:59:00+00:00","drawdown":-14.615617370734324},{"timestamp":"2023-09-29T23:59:00+00:00","drawdown":-12.624859800238328},{"timestamp":"2023-10-06T23:59:00+00:00","drawdown":-10.3481237518331},{"timestamp":"2023-10-12T23:59:00+00:00","drawdown":-17.1439174630785},{"timestamp":"2023-10-18T23:59:00+00:00","drawdown":-15.173364937354156},{"timestamp":"2023-10-24T23:59:00+00:00","drawdown":0},{"timestamp":"2023-10-30T23:59:00+00:00","drawdown":-0.8411992795257806},{"timestamp":"2023-11-05T23:59:00+00:00","drawdown":-1.8303319989031497},{"timestamp":"2023-11-12T23:59:00+00:00","drawdown":0},{"timestamp":"2023-11-18T23:59:00+00:00","drawdown":-4.342898706670642},{"timestamp":"2023-11-24T23:59:00+00:00","drawdown":0},{"timestamp":"2023-11-30T23:59:00+00:00","drawdown":-2.8098226997512605},{"timestamp":"2023-12-06T23:59:00+00:00","drawdown":-6.785106741363929},{"timestamp":"2023-12-12T23:59:00+00:00","drawdown":-5.847090848132896},{"timestamp":"2023-12-19T23:59:00+00:00","drawdown":-5.620099186097311},{"timestamp":"2023-12-25T23:59:00+00:00","drawdown":-1.700872654840981},{"timestamp":"2023-12-31T23:59:00+00:00","drawdown":-4.7260609547499834}]

​
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).
StatValue
Best Unrealized583.30 USDT @ 2023-11-14 23:59:00 UTC
Worst Unrealized-6,543.00 USDT @ 2023-06-14 23:59:00 UTC
Mean-2,194.98 USDT
Std Dev1,651.55 USDT
Last-1,418.41 USDT
Points365
Sum-801,166.98 USDT
Across 365 points the series ranged from -6,543.00 USDT (2023-06-14 23:59:00 UTC) to 583.30 USDT (2023-11-14 23:59:00 UTC), averaging -2,194.98 USDT. 10% of points were positive, 90% negative. The most recent value is -1,418.41 USDT.
Showing 60 of 365 points (down-sampled for readability).
Unrealized PnL
[{"timestamp":"2023-01-01T23:59:00+00:00","pnl":6.413006702},{"timestamp":"2023-01-07T23:59:00+00:00","pnl":17.9798523007},{"timestamp":"2023-01-13T23:59:00+00:00","pnl":45.8449083345},{"timestamp":"2023-01-20T23:59:00+00:00","pnl":28.9726223108},{"timestamp":"2023-01-26T23:59:00+00:00","pnl":217.9475365604},{"timestamp":"2023-02-01T23:59:00+00:00","pnl":540.3949663537},{"timestamp":"2023-02-07T23:59:00+00:00","pnl":284.7863697745},{"timestamp":"2023-02-13T23:59:00+00:00","pnl":-1619.5778249411},{"timestamp":"2023-02-19T23:59:00+00:00","pnl":-380.9768131299},{"timestamp":"2023-02-26T23:59:00+00:00","pnl":-280.2856189301},{"timestamp":"2023-03-04T23:59:00+00:00","pnl":-1498.7785643501},{"timestamp":"2023-03-10T23:59:00+00:00","pnl":-3294.9857225844},{"timestamp":"2023-03-16T23:59:00+00:00","pnl":-1530.7996228118},{"timestamp":"2023-03-22T23:59:00+00:00","pnl":-1409.0303139776},{"timestamp":"2023-03-28T23:59:00+00:00","pnl":-1291.1849562152},{"timestamp":"2023-04-04T23:59:00+00:00","pnl":-1397.0114133736},{"timestamp":"2023-04-10T23:59:00+00:00","pnl":-1183.7010719004},{"timestamp":"2023-04-16T23:59:00+00:00","pnl":-354.7656155432},{"timestamp":"2023-04-22T23:59:00+00:00","pnl":-1127.1825301706},{"timestamp":"2023-04-28T23:59:00+00:00","pnl":-600.6236621991},{"timestamp":"2023-05-04T23:59:00+00:00","pnl":-1326.2592684798},{"timestamp":"2023-05-11T23:59:00+00:00","pnl":-3609.3291004782},{"timestamp":"2023-05-17T23:59:00+00:00","pnl":-2424.6754585955},{"timestamp":"2023-05-23T23:59:00+00:00","pnl":-2807.406785839},{"timestamp":"2023-05-29T23:59:00+00:00","pnl":-3375.4484717973},{"timestamp":"2023-06-04T23:59:00+00:00","pnl":-2861.5567728211},{"timestamp":"2023-06-10T23:59:00+00:00","pnl":-6293.4481283209},{"timestamp":"2023-06-17T23:59:00+00:00","pnl":-5566.9877127976},{"timestamp":"2023-06-23T23:59:00+00:00","pnl":-4250.7938323096},{"timestamp":"2023-06-29T23:59:00+00:00","pnl":-3704.8073719956},{"timestamp":"2023-07-05T23:59:00+00:00","pnl":-4260.8449537822},{"timestamp":"2023-07-11T23:59:00+00:00","pnl":-4444.0816902149},{"timestamp":"2023-07-17T23:59:00+00:00","pnl":-2885.2865253006},{"timestamp":"2023-07-24T23:59:00+00:00","pnl":-3248.2942292603},{"timestamp":"2023-07-30T23:59:00+00:00","pnl":-3404.4246614084},{"timestamp":"2023-08-05T23:59:00+00:00","pnl":-3286.1138241521},{"timestamp":"2023-08-11T23:59:00+00:00","pnl":-2753.2150909192},{"timestamp":"2023-08-17T23:59:00+00:00","pnl":-4384.7798444269},{"timestamp":"2023-08-23T23:59:00+00:00","pnl":-2814.80190098},{"timestamp":"2023-08-30T23:59:00+00:00","pnl":-2934.484155733},{"timestamp":"2023-09-05T23:59:00+00:00","pnl":-3032.0795970325},{"timestamp":"2023-09-11T23:59:00+00:00","pnl":-4575.1411663833},{"timestamp":"2023-09-17T23:59:00+00:00","pnl":-4171.6733971303},{"timestamp":"2023-09-23T23:59:00+00:00","pnl":-4294.5128593486},{"timestamp":"2023-09-29T23:59:00+00:00","pnl":-3985.4523657131},{"timestamp":"2023-10-06T23:59:00+00:00","pnl":-3708.5101023878},{"timestamp":"2023-10-12T23:59:00+00:00","pnl":-4846.7592617711},{"timestamp":"2023-10-18T23:59:00+00:00","pnl":-4533.3503103414},{"timestamp":"2023-10-24T23:59:00+00:00","pnl":-2273.7865514183},{"timestamp":"2023-10-30T23:59:00+00:00","pnl":-1548.1260301653},{"timestamp":"2023-11-05T23:59:00+00:00","pnl":-1811.4180065847},{"timestamp":"2023-11-12T23:59:00+00:00","pnl":191.1885298774},{"timestamp":"2023-11-18T23:59:00+00:00","pnl":-539.6389489135},{"timestamp":"2023-11-24T23:59:00+00:00","pnl":74.6113467096},{"timestamp":"2023-11-30T23:59:00+00:00","pnl":-578.157334619},{"timestamp":"2023-12-06T23:59:00+00:00","pnl":-1559.7087231301},{"timestamp":"2023-12-12T23:59:00+00:00","pnl":-1554.1699108011},{"timestamp":"2023-12-19T23:59:00+00:00","pnl":-1831.5949703519},{"timestamp":"2023-12-25T23:59:00+00:00","pnl":-1088.8887290098},{"timestamp":"2023-12-31T23:59:00+00:00","pnl":-1418.4141775756}]

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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).
StatValue
Peak Total Equity21,765.39 USDT @ 2023-12-27 23:59:00 UTC
Trough Total Equity10,015.02 USDT @ 2023-01-01 23:59:00 UTC
Mean14,615.90 USDT
Std Dev2,805.56 USDT
Last20,736.74 USDT
Points365
Sum5,334,801.91 USDT
Across 365 points the series ranged from 10,015.02 USDT (2023-01-01 23:59:00 UTC) to 21,765.39 USDT (2023-12-27 23:59:00 UTC), averaging 14,615.90 USDT. 100% of points were positive, 0% negative. The most recent value is 20,736.74 USDT.
Showing 60 of 365 points (down-sampled for readability).
Balance Composition (Cash vs. Coin)
[{"timestamp":"2023-01-01T23:59:00+00:00","quote":9217.6447286698,"base":797.3787171000004,"total":10015.0234457698},{"timestamp":"2023-01-07T23:59:00+00:00","quote":8704.7840310863,"base":1404.6903315199997,"total":10109.4743626063},{"timestamp":"2023-01-13T23:59:00+00:00","quote":9053.5465185395,"base":1362.6318190799993,"total":10416.1783376195},{"timestamp":"2023-01-20T23:59:00+00:00","quote":9837.5045890632,"base":1015.4562761700017,"total":10852.9608652332},{"timestamp":"2023-01-26T23:59:00+00:00","quote":9931.0534701253,"base":1965.9114197800009,"total":11896.9648899053},{"timestamp":"2023-02-01T23:59:00+00:00","quote":10430.2534280696,"base":2689.708104825,"total":13119.9615328946},{"timestamp":"2023-02-07T23:59:00+00:00","quote":7505.120581535,"base":5656.014105524999,"total":13161.13468706},{"timestamp":"2023-02-13T23:59:00+00:00","quote":1192.3816206651,"base":10284.570083024999,"total":11476.9517036901},{"timestamp":"2023-02-19T23:59:00+00:00","quote":6292.0591549847,"base":6924.49481094,"total":13216.5539659247},{"timestamp":"2023-02-26T23:59:00+00:00","quote":5591.4954048793,"base":7981.952482420001,"total":13573.4478872993},{"timestamp":"2023-03-04T23:59:00+00:00","quote":4125.6230242592,"base":8516.437048122501,"total":12642.0600723817},{"timestamp":"2023-03-10T23:59:00+00:00","quote":7.7962388592,"base":10927.671275115099,"total":10935.4675139743},{"timestamp":"2023-03-16T23:59:00+00:00","quote":4697.3681878637,"base":8504.24043245,"total":13201.6086203137},{"timestamp":"2023-03-22T23:59:00+00:00","quote":4076.6804561943,"base":9758.7254624999,"total":13835.4059186942},{"timestamp":"2023-03-28T23:59:00+00:00","quote":3902.2171429801,"base":10247.2427995,"total":14149.4599424801},{"timestamp":"2023-04-04T23:59:00+00:00","quote":4016.2496908316,"base":10141.3021246426,"total":14157.5518154742},{"timestamp":"2023-04-10T23:59:00+00:00","quote":4531.7062862221,"base":9953.9948205601,"total":14485.7011067822},{"timestamp":"2023-04-16T23:59:00+00:00","quote":8073.506563101,"base":7486.637747295199,"total":15560.1443103962},{"timestamp":"2023-04-22T23:59:00+00:00","quote":5754.2506467238,"base":9248.5252898326,"total":15002.7759365564},{"timestamp":"2023-04-28T23:59:00+00:00","quote":7514.132643489,"base":8222.0574085651,"total":15736.1900520541},{"timestamp":"2023-05-04T23:59:00+00:00","quote":5542.5297107667,"base":9515.238774580099,"total":15057.7684853468},{"timestamp":"2023-05-11T23:59:00+00:00","quote":472.338073221,"base":12345.998241482699,"total":12818.3363147037},{"timestamp":"2023-05-17T23:59:00+00:00","quote":2833.7231467811,"base":11336.927262745301,"total":14170.6504095264},{"timestamp":"2023-05-23T23:59:00+00:00","quote":2397.2586592196,"base":11410.0044037528,"total":13807.2630629724},{"timestamp":"2023-05-29T23:59:00+00:00","quote":1438.5984345607,"base":11833.686083992801,"total":13272.2845185535},{"timestamp":"2023-06-04T23:59:00+00:00","quote":2134.9457447876,"base":11701.441192105402,"total":13836.386936893},{"timestamp":"2023-06-10T23:59:00+00:00","quote":1.0774403526,"base":10588.3882683604,"total":10589.465708713},{"timestamp":"2023-06-17T23:59:00+00:00","quote":4.4478485323,"base":11324.9028278404,"total":11329.3506763727},{"timestamp":"2023-06-23T23:59:00+00:00","quote":1.3166747754,"base":12671.1569494129,"total":12672.4736241883},{"timestamp":"2023-06-29T23:59:00+00:00","quote":794.5954146465,"base":12525.9667937604,"total":13320.5622084069},{"timestamp":"2023-07-05T23:59:00+00:00","quote":15.5343333154,"base":12891.5425461555,"total":12907.0768794709},{"timestamp":"2023-07-11T23:59:00+00:00","quote":0.7999059245,"base":12728.308153838001,"total":12729.1080597625},{"timestamp":"2023-07-17T23:59:00+00:00","quote":3345.8637256269,"base":11176.9229560456,"total":14522.7866816725},{"timestamp":"2023-07-24T23:59:00+00:00","quote":3024.441661843,"base":11244.7668649232,"total":14269.2085267662},{"timestamp":"2023-07-30T23:59:00+00:00","quote":2298.8513599703,"base":11824.9339929882,"total":14123.7853529585},{"timestamp":"2023-08-05T23:59:00+00:00","quote":2029.8372082542,"base":12288.563379960699,"total":14318.4005882149},{"timestamp":"2023-08-11T23:59:00+00:00","quote":3428.1220745303,"base":11519.2604702008,"total":14947.3825447311},{"timestamp":"2023-08-17T23:59:00+00:00","quote":723.7221294304,"base":12632.4599839207,"total":13356.1821133511},{"timestamp":"2023-08-23T23:59:00+00:00","quote":3086.7803295056,"base":12003.6425110009,"total":15090.4228405065},{"timestamp":"2023-08-30T23:59:00+00:00","quote":3879.1315672343,"base":11222.9737720109,"total":15102.1053392452},{"timestamp":"2023-09-05T23:59:00+00:00","quote":3101.7469572128,"base":11956.817875836,"total":15058.5648330488},{"timestamp":"2023-09-11T23:59:00+00:00","quote":8.6219347683,"base":13518.8211940608,"total":13527.4431288291},{"timestamp":"2023-09-17T23:59:00+00:00","quote":650.6341751317,"base":13321.280500781,"total":13971.9146759127},{"timestamp":"2023-09-23T23:59:00+00:00","quote":579.6888597727,"base":13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​
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).
StatValue
Peak Exposure100.00% @ 2023-07-06 23:59:00 UTC
Min Exposure7.16% @ 2023-11-14 23:59:00 UTC
Mean67.97%
Std Dev25.19%
Last46.37%
Points365
Across 365 points the series ranged from 7.16% (2023-11-14 23:59:00 UTC) to 100.00% (2023-07-06 23:59:00 UTC), averaging 67.97%. 100% of points were positive, 0% negative. The most recent value is 46.37%.
Showing 60 of 365 points (down-sampled for readability).
Base Exposure (% of Portfolio)
[{"timestamp":"2023-01-01T23:59:00+00:00","exposure":7.9618257652387365},{"timestamp":"2023-01-07T23:59:00+00:00","exposure":13.89479097662858},{"timestamp":"2023-01-13T23:59:00+00:00","exposure":13.081878736260322},{"timestamp":"2023-01-20T23:59:00+00:00","exposure":9.356490719716442},{"timestamp":"2023-01-26T23:59:00+00:00","exposure":16.524478620997673},{"timestamp":"2023-02-01T23:59:00+00:00","exposure":20.500884077146996},{"timestamp":"2023-02-07T23:59:00+00:00","exposure":42.975125169761995},{"timestamp":"2023-02-13T23:59:00+00:00","exposure":89.61064181979852},{"timestamp":"2023-02-19T23:59:00+00:00","exposure":52.39258908784341},{"timestamp":"2023-02-26T23:59:00+00:00","exposure":58.80563692213183},{"timestamp":"2023-03-04T23:59:00+00:00","exposure":67.36589605935995},{"timestamp":"2023-03-10T23:59:00+00:00","exposure":99.92870685364629},{"timestamp":"2023-03-16T23:59:00+00:00","exposure":64.41821354531203},{"timestamp":"2023-03-22T23:59:00+00:00","exposure":70.53443548999205},{"timestamp":"2023-03-28T23:59:00+00:00","exposure":72.42144110910763},{"timestamp":"2023-04-04T23:59:00+00:00","exposure":71.63175001456226},{"timestamp":"2023-04-10T23:59:00+00:00","exposure":68.71600309286819},{"timestamp":"2023-04-16T23:59:00+00:00","exposure":48.114192246232264},{"timestamp":"2023-04-22T23:59:00+00:00","exposure":61.64542701259206},{"timestamp":"2023-04-28T23:59:00+00:00","exposure":52.24935248854501},{"timestamp":"2023-05-04T23:59:00+00:00","exposure":63.19155978417177},{"timestamp":"2023-05-11T23:59:00+00:00","exposure":96.31513745914756},{"timestamp":"2023-05-17T23:59:00+00:00","exposure":80.00287167569886},{"timestamp":"2023-05-23T23:59:00+00:00","exposure":82.63769837449941},{"timestamp":"2023-05-29T23:59:00+00:00","exposure":89.16088309778422},{"timestamp":"2023-06-04T23:59:00+00:00","exposure":84.57006330825403},{"timestamp":"2023-06-10T23:59:00+00:00","exposure":99.98982535680045},{"timestamp":"2023-06-17T23:59:00+00:00","exposure":99.96074048143309},{"timestamp":"2023-06-23T23:59:00+00:00","exposure":99.98960996239214},{"timestamp":"2023-06-29T23:59:00+00:00","exposure":94.0348207364325},{"timestamp":"2023-07-05T23:59:00+00:00","exposure":99.8796448377858},{"timestamp":"2023-07-11T23:59:00+00:00","exposure":99.99371593107118},{"timestamp":"2023-07-17T23:59:00+00:00","exposure":76.96128298951523},{"timestamp":"2023-07-24T23:59:00+00:00","exposure":78.80441892646148},{"timestamp":"2023-07-30T23:59:00+00:00","exposure":83.72354646774095},{"timestamp":"2023-08-05T23:59:00+00:00","exposure":85.82357578454044},{"timestamp":"2023-08-11T23:59:00+00:00","exposure":77.06540215806076},{"timestamp":"2023-08-17T23:59:00+00:00","exposure":94.5813697111321},{"timestamp":"2023-08-23T23:59:00+00:00","exposure":79.54477245515014},{"timestamp":"2023-08-30T23:59:00+00:00","exposure":74.31396828392023},{"timestamp":"2023-09-05T23:59:00+00:00","exposure":79.40210775992782},{"timestamp":"2023-09-11T23:59:00+00:00","exposure":99.93626338188089},{"timestamp":"2023-09-17T23:59:00+00:00","exposure":95.34327119637096},{"timestamp":"2023-09-23T23:59:00+00:00","exposure":95.82912403536542},{"timestamp":"2023-09-29T23:59:00+00:00","exposure":94.83235774876245},{"timestamp":"2023-10-06T23:59:00+00:00","exposure":87.93833895043292},{"timestamp":"2023-10-12T23:59:00+00:00","exposure":99.93081089225781},{"timestamp":"2023-10-18T23:59:00+00:00","exposure":99.40758309060037},{"timestamp":"2023-10-24T23:59:00+00:00","exposure":63.05810332373216},{"timestamp":"2023-10-30T23:59:00+00:00","exposure":57.50575545670037},{"timestamp":"2023-11-05T23:59:00+00:00","exposure":63.55025882669598},{"timestamp":"2023-11-12T23:59:00+00:00","exposure":21.479849631412335},{"timestamp":"2023-11-18T23:59:00+00:00","exposure":38.02151536853228},{"timestamp":"2023-11-24T23:59:00+00:00","exposure":28.93227987961688},{"timestamp":"2023-11-30T23:59:00+00:00","exposure":40.801738846387536},{"timestamp":"2023-12-06T23:59:00+00:00","exposure":50.150324831370405},{"timestamp":"2023-12-12T23:59:00+00:00","exposure":54.32645977722059},{"timestamp":"2023-12-19T23:59:00+00:00","exposure":53.383104126124614},{"timestamp":"2023-12-25T23:59:00+00:00","exposure":44.89275924145939},{"timestamp":"2023-12-31T23:59:00+00:00","exposure":46.37324704436621}]

​
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).
StatValue
Best Day1,894.04 USDT @ 2023-03-12 23:59:00 UTC
Worst Day-2,452.88 USDT @ 2023-06-10 23:59:00 UTC
Mean29.46 USDT
Std Dev455.57 USDT
Last-224.89 USDT
Points364
Sum10,721.72 USDT
Across 364 points the series ranged from -2,452.88 USDT (2023-06-10 23:59:00 UTC) to 1,894.04 USDT (2023-03-12 23:59:00 UTC), averaging 29.46 USDT. 53% of points were positive, 47% negative. The most recent value is -224.89 USDT.
Showing 60 of 364 points (down-sampled for readability).
Daily Change
[{"timestamp":"2023-01-02T23:59:00+00:00","delta":31.852427081399583},{"timestamp":"2023-01-08T23:59:00+00:00","delta":50.76208280730134},{"timestamp":"2023-01-14T23:59:00+00:00","delta":81.11146949790054},{"timestamp":"2023-01-20T23:59:00+00:00","delta":709.4137273014003},{"timestamp":"2023-01-27T23:59:00+00:00","delta":161.27572620529827},{"timestamp":"2023-02-02T23:59:00+00:00","delta":-133.26868119060055},{"timestamp":"2023-02-08T23:59:00+00:00","delta":-410.5210262405999},{"timestamp":"2023-02-14T23:59:00+00:00","delta":826.6833620035995},{"timestamp":"2023-02-20T23:59:00+00:00","delta":231.5291932821001},{"timestamp":"2023-02-26T23:59:00+00:00","delta":54.08757883910039},{"timestamp":"2023-03-05T23:59:00+00:00","delta":92.2964225727992},{"timestamp":"2023-03-11T23:59:00+00:00","delta":-654.6850027595992},{"timestamp":"2023-03-17T23:59:00+00:00","delta":1058.1981487819994},{"timestamp":"2023-03-23T23:59:00+00:00","delta":485.92864222860044},{"timestamp":"2023-03-29T23:59:00+00:00","delta":-7.292730301100164},{"timestamp":"2023-04-04T23:59:00+00:00","delta":172.42724723170068},{"timestamp":"2023-04-10T23:59:00+00:00","delta":410.84728357449967},{"timestamp":"2023-04-17T23:59:00+00:00","delta":302.4441023127001},{"timestamp":"2023-04-23T23:59:00+00:00","delta":-514.7185789199993},{"timestamp":"2023-04-29T23:59:00+00:00","delta":-148.10834371710007},{"timestamp":"2023-05-05T23:59:00+00:00","delta":270.2505693141993},{"timestamp":"2023-05-11T23:59:00+00:00","delta":-1060.628435000801},{"timestamp":"2023-05-17T23:59:00+00:00","delta":193.7550009680017},{"timestamp":"2023-05-24T23:59:00+00:00","delta":-661.8753876544015},{"timestamp":"2023-05-30T23:59:00+00:00","delta":528.3945048521},{"timestamp":"2023-06-05T23:59:00+00:00","delta":-594.6510902067002},{"timestamp":"2023-06-11T23:59:00+00:00","delta":-200.15856840000015},{"timestamp":"2023-06-17T23:59:00+00:00","delta":13.18823949130092},{"timestamp":"2023-06-23T23:59:00+00:00","delta":504.752821518101},{"timestamp":"2023-06-29T23:59:00+00:00","delta":802.5538685857991},{"timestamp":"2023-07-06T23:59:00+00:00","delta":-626.9722319048997},{"timestamp":"2023-07-12T23:59:00+00:00","delta":-190.48243094999998},{"timestamp":"2023-07-18T23:59:00+00:00","delta":-604.5808996397991},{"timestamp":"2023-07-24T23:59:00+00:00","delta":-577.1958513946993},{"timestamp":"2023-07-30T23:59:00+00:00","delta":-173.2897540385984},{"timestamp":"2023-08-05T23:59:00+00:00","delta":613.4120315484997},{"timestamp":"2023-08-11T23:59:00+00:00","delta":-135.80395993599996},{"timestamp":"2023-08-18T23:59:00+00:00","delta":565.5488327351995},{"timestamp":"2023-08-24T23:59:00+00:00","delta":195.49383708329879},{"timestamp":"2023-08-30T23:59:00+00:00","delta":-230.36070348090107},{"timestamp":"2023-09-05T23:59:00+00:00","delta":211.11492778409956},{"timestamp":"2023-09-11T23:59:00+00:00","delta":-448.2957652383993},{"timestamp":"2023-09-17T23:59:00+00:00","delta":-550.9541217935002},{"timestamp":"2023-09-24T23:59:00+00:00","delta":-224.6286196694},{"timestamp":"2023-09-30T23:59:00+00:00","delta":172.8123491615006},{"timestamp":"2023-10-06T23:59:00+00:00","delta":466.45419598789886},{"timestamp":"2023-10-12T23:59:00+00:00","delta":-265.51098855860073},{"timestamp":"2023-10-18T23:59:00+00:00","delta":354.29356655279844},{"timestamp":"2023-10-24T23:59:00+00:00","delta":1070.9058951107982},{"timestamp":"2023-10-30T23:59:00+00:00","delta":-147.22314015919983},{"timestamp":"2023-11-06T23:59:00+00:00","delta":516.2175813446011},{"timestamp":"2023-11-12T23:59:00+00:00","delta":555.2144160085008},{"timestamp":"2023-11-18T23:59:00+00:00","delta":-302.7371003590997},{"timestamp":"2023-11-24T23:59:00+00:00","delta":586.0075786902999},{"timestamp":"2023-11-30T23:59:00+00:00","delta":39.31658151490046},{"timestamp":"2023-12-06T23:59:00+00:00","delta":-537.062958631599},{"timestamp":"2023-12-13T23:59:00+00:00","delta":-119.90152938900064},{"timestamp":"2023-12-19T23:59:00+00:00","delta":-325.6986163490001},{"timestamp":"2023-12-25T23:59:00+00:00","delta":201.69368592779938},{"timestamp":"2023-12-31T23:59:00+00:00","delta":-224.88812169399898}]

​
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).
StatValue
Best Month29.24% @ 2023-01
Worst Month-10.51% @ 2023-05
Mean7.00%
Std Dev10.45%
Last3.46%
Points12
Across 12 points the series ranged from -10.51% (2023-05) to 29.24% (2023-01), averaging 7.00%. 75% of points were positive, 25% negative. The most recent value is 3.46%.
Full series - 12 points.
Monthly Returns
[
  {
    "month": "2023-01",
    "returnPct": 29.238287043619827
  },
  {
    "month": "2023-02",
    "returnPct": 5.418082591685527
  },
  {
    "month": "2023-03",
    "returnPct": -2.4406810331181705
  },
  {
    "month": "2023-04",
    "returnPct": 10.237111652395837
  },
  {
    "month": "2023-05",
    "returnPct": -10.506310959813572
  },
  {
    "month": "2023-06",
    "returnPct": 5.6387070773087
  },
  {
    "month": "2023-07",
    "returnPct": 3.0609738314309136
  },
  {
    "month": "2023-08",
    "returnPct": 10.56616704790243
  },
  {
    "month": "2023-09",
    "returnPct": -5.046740194677793
  },
  {
    "month": "2023-10",
    "returnPct": 14.962699243460282
  },
  {
    "month": "2023-11",
    "returnPct": 19.4554309387587
  },
  {
    "month": "2023-12",
    "returnPct": 3.460463187191244
  }
]

​
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.
facts.json
{
  "backtest_id": "f5b83fa5-668f-4498-adf6-94c6c288fa03",
  "symbol": "DYDXUSDT",
  "strategy": "1FullBullMarket.json",
  "strategy_label": "FullBullMarket",
  "status": "completed",
  "period_from": "2023-01-01",
  "period_to": "2023-12-31",
  "duration_days": 364,
  "start_balance_usdt": 10000,
  "final_value_usdt": 20736.7447252,
  "return_pct": 107.3674,
  "total_profit_usdt": 12279.12650165,
  "avg_profit_per_trade_usdt": 0.13566297,
  "fulfilled_trades": 90512,
  "active_orders": 965,
  "win_rate_pct": 100,
  "best_trade_usdt": 1.48007255,
  "worst_trade_usdt": 0.00994695,
  "trades_per_month": 7547.8333553927805,
  "max_drawdown_pct": 36.460696786160064,
  "max_drawdown_abs_usdt": 5934.910867834,
  "drawdown_duration_days": 57,
  "recovery_days": 132,
  "cagr_pct": 107.88745830717272,
  "volatility_annualized_pct": 63.34716414624639,
  "sharpe_annualized": 1.4704851780561925,
  "sortino_annualized": 1.5278972575717429,
  "time_in_market_pct": 100,
  "final_cash_usdt": 11120.4428648238,
  "final_base_value_usdt": null,
  "final_unrealized_pnl_usdt": -1418.4141775756,
  "median_hold_hours": 12.733333333333333,
  "avg_hold_hours": 679.9352059925096,
  "buyhold_return_pct": 167.45240253853132,
  "outperformance_vs_buyhold_pp": -60.08500253853131
}

​
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.
MetricSourceJSON pathTransformValue in this report
SymbolGET /backtests/{id}$.symboldirect API valueDYDXUSDT
ModeGET /backtests/{id}$.mode_namedirect API value1FullBullMarket.json
StatusGET /backtests/{id}$.statusdirect API valuecompleted
Period startGET /backtests/{id}$.config.fromdirect API value2023-01-01 00:00:01
Period endGET /backtests/{id}$.config.todirect API value2023-12-31 23:59:59
Start balanceGET /backtests/{id}$.start_balancedirect API value10,000.00 USDT
Final valueGET /backtests/{id}$.final_valuedirect API value20,736.74 USDT
Return %GET /backtests/{id}$.return_pctdirect API value (cross-check: (final_value/start_balance - 1) * 100)+107.37%
Total profitGET /backtests/{id}$.total_profitdirect API value (cross-check: Sum trades[].profit)+12,279.13 USDT
Win rate %GET /backtests/{id}$.win_ratedirect API value (cross-check: 100 * count(trades[].profit > 0) / count(trades))100.0%
Trades countGET /backtests/{id}$.fulfilled_tradesdirect API value (cross-check: count(trades))90,512
Best tradeGET /backtests/{id}$.max_profitdirect API value (cross-check: max(trades[].profit))+1.4801 USDT
Worst tradeGET /backtests/{id}$.min_profitdirect API value (cross-check: min(trades[].profit))+0.0099 USDT
Avg profit / tradeGET /backtests/{id}$.avg_profitdirect API value+0.1357 USDT
Trades / monthderivedn/afulfilled_trades / (durationDays / 30.4375)7547.8
Max drawdown %GET /backtests/{id}/equity$[*].total_value_*maxDrawdown(equity) - running peak vs. trough-36.46%
Max drawdown absGET /backtests/{id}/equity$[*].total_value_*maxDrawdown(equity).abs-5,934.91 USDT
CAGR %GET /backtests/{id}/equity$[*].timestamp, $[*].total_value_*(final/start)^(365.25/days) - 1+107.89%
Volatility (ann.) %GET /backtests/{id}/equity$[*].timestamp, $[*].total_value_*std(daily_returns) * sqrt(365)63.35%
Sharpe ratio (ann.)GET /backtests/{id}/equity$[*].timestamp, $[*].total_value_*mean(daily_returns) / std(daily_returns) * sqrt(365) (rf=0)1.47
Sortino ratio (ann.)GET /backtests/{id}/equity$[*].timestamp, $[*].total_value_*mean(daily_returns) / std(downside_returns) * sqrt(365) (rf=0)1.53
Time in market %GET /backtests/{id}/equity$[*].base_asset_balcount(samples where base_asset_bal>0) / count(samples) * 100100.0%
Total feesGET /backtests/{id}/trades$[*].buy_fee_in_quote, $[*].sell_fee_in_quoteSum (buy_fee_in_quote + sell_fee_in_quote)1.77 USDT
Gross profit (winners)GET /backtests/{id}/trades$[?(@.profit>0)].profitSum profit where profit > 0+50.52 USDT
Gross loss (losers)GET /backtests/{id}/trades$[?(@.profit<0)].profitSum abs(profit) where profit < 00.00 USDT
Profit factorGET /backtests/{id}/trades$[*].profitSum winners.profit / Sum abs(losers.profit)inf
Payoff ratioGET /backtests/{id}/trades$[*].profitavg(winners.profit) / avg(abs(losers.profit))inf
Expectancy / tradeGET /backtests/{id}/trades$[*].profitmean(trades[].profit)+0.5052 USDT
Max consecutive winsGET /backtests/{id}/trades$[*].profit (ordered)longest run where profit > 0100
Max consecutive lossesGET /backtests/{id}/trades$[*].profit (ordered)longest run where profit < 00
Avg holding (h)GET /backtests/{id}/trades$[*].buy_time, $[*].sell_timemean(sell_time - buy_time) / 3600679.9h
Median holding (h)GET /backtests/{id}/trades$[*].buy_time, $[*].sell_timemedian(sell_time - buy_time) / 360012.7h
Total volume (notional)GET /backtests/{id}/trades$[*].buy_quantity, $[*].buy_priceSum (buy_quantity * buy_price)2,365 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: 90,512 closed trades - fetch the complete list via GET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03/trades. Aggregate KPIs are computed server-side against the full population, not from this sample.
backtest.json
{
  "id": "f5b83fa5-668f-4498-adf6-94c6c288fa03",
  "status": "completed",
  "error_message": null,
  "created_at": "2026-04-18T10:03:54.276911Z",
  "started_at": "2026-04-19T11:15:50.002403Z",
  "completed_at": "2026-04-19T11:56:06.602896Z",
  "mode_name": "1FullBullMarket.json",
  "symbol": "DYDXUSDT",
  "data_file": "",
  "start_balance": 10000,
  "final_value": 20736.7447252,
  "return_pct": 107.3674,
  "total_profit": 12279.12650165,
  "fulfilled_trades": 90512,
  "active_orders": 965,
  "win_rate": 100,
  "avg_profit": 0.13566297,
  "max_profit": 1.48007255,
  "min_profit": 0.00994695,
  "elapsed_sec": 2250.64,
  "max_drawdown": -36.4607,
  "profit_factor": null,
  "sharpe_ratio": 1.4705,
  "total_fees": 1396.54234753,
  "avg_hold_time_hours": 91.7357,
  "tp_count": 90512,
  "sl_count": 0,
  "tsl_count": 0,
  "start_price": 1.14,
  "end_price": 2.95,
  "gross_profit_quote": 13675.668849,
  "gross_loss_quote": 0,
  "wins": 90512,
  "losses": 0,
  "breakeven": 0,
  "consecutive_wins_max": 90512,
  "consecutive_losses_max": 0,
  "holding_minutes_p50": 25,
  "holding_minutes_p95": 10299.45,
  "holding_minutes_max": 412052,
  "description": "DYDXUSDT | 1FullBullMarket.json | 2023-01-01 - 2023-12-31 | +107.37% | 90512 trades | 100% WR",
  "summary_text": "Backtest DYDXUSDT (Mode: 1FullBullMarket.json)\nPeriod: 2023-01-01 00:00:01 to 2023-12-31 23:59:59\nStarting balance: 10,000.00 USDT\nFinal value: 20,736.74 USDT\nP&L: +10,736.74 USDT (+107.37%)\nResult: PROFIT\nCompleted trades: 90512\nOpen orders at end: 965\nWin rate: 100.0%\nAvg. profit/trade: 0.135663 USDT\nBest trade: 1.480073 USDT\nWorst trade: 0.009947 USDT\nTotal profit (trades only): 12,279.126502 USDT\n\nStrategy parameters:\n  Buy trigger: -0.1% from last buy\n  Buy splits: 6\n  Sell targets: [0.25, 2.5, 5.0, 10.0, 20.0, 30.0]\n  Investment per buy: 50.0 USDT\n  Fees: maker 7.5 bps / taker 7.5 bps\n\nElapsed: 2250.6s",
  "share_token": null,
  "config_hash": "31f23f3588a9df36b60860a1cf8b2a866b6450f0c28ec72a18c6b8a7d41b3794",
  "config": {
    "to": "2023-12-31 23:59:59",
    "from": "2023-01-01 00:00:01",
    "canBuy": true,
    "symbol": "DYDXUSDT",
    "canSell": true,
    "canBuyUp": true,
    "startBal": 10000,
    "stepSize": 0.01,
    "stopLoss": false,
    "tickSize": 0.00001,
    "buySplits": 6,
    "buyVolumes": [
      20,
      20,
      20,
      30,
      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,
      2.5,
      5,
      10,
      20,
      30
    ],
    "triggerCoolDown": 1,
    "investmentPerBuy": 50,
    "assumed_spread_bps": 0,
    "stopLossPercentage": 5,
    "investmentPercentMode": false,
    "minInvestmentPerQuote": 20,
    "order_latency_seconds": 2,
    "trailingStopLossPercentages": [
      0,
      0,
      0,
      0,
      0,
      0
    ],
    "sellCancelDistancePercentage": 1,
    "dontBuyBelowQuoteAssetBalance": 1,
    "investmentPerFreeQuotePercent": 0.01,
    "sellActivateDistancePercentage": 0.1
  },
  "is_duplicate": false,
  "compact": false
}
equity.json
[
  {
    "timestamp": "2023-01-01T23:59:00+00:00",
    "base_asset_bal": 699.98,
    "quote_asset_bal": 9217.6447286698,
    "total_value_mid": 10015.6219286698,
    "total_value_exit_net": 10015.0234457698,
    "total_value": 10015.6219286698,
    "base_cost_quote": 790.965710398,
    "unrealized_pnl_exit_net": 6.413006702
  },
  {
    "timestamp": "2023-01-02T23:59:00+00:00",
    "base_asset_bal": 779.4,
    "quote_asset_bal": 9126.3160109512,
    "total_value_mid": 10047.5668109512,
    "total_value_exit_net": 10046.8758728512,
    "total_value": 10047.5668109512,
    "base_cost_quote": 906.0965065826,
    "unrealized_pnl_exit_net": 14.4633553174
  },
  {
    "timestamp": "2023-01-03T23:59:00+00:00",
    "base_asset_bal": 1167.67,
    "quote_asset_bal": 8677.0354900643,
    "total_value_mid": 10035.0357000643,
    "total_value_exit_net": 10034.0171999068,
    "total_value": 10035.0357000643,
    "base_cost_quote": 1356.7055234677,
    "unrealized_pnl_exit_net": 0.2761863748
  },
  {
    "timestamp": "2023-01-04T23:59:00+00:00",
    "base_asset_bal": 1050.03,
    "quote_asset_bal": 8818.6966370551,
    "total_value_mid": 10056.6820070551,
    "total_value_exit_net": 10055.7535180276,
    "total_value": 10056.6820070551,
    "base_cost_quote": 1221.5143792976,
    "unrealized_pnl_exit_net": 15.5425016749
  },
  {
    "timestamp": "2023-01-05T23:59:00+00:00",
    "base_asset_bal": 1448.45,
    "quote_asset_bal": 8343.5362068366,
    "total_value_mid": 10051.2587568366,
    "total_value_exit_net": 10049.9779649241,
    "total_value": 10051.2587568366,
    "base_cost_quote": 1732.1386365796,
    "unrealized_pnl_exit_net": -25.6968784921
  },
  {
    "timestamp": "2023-01-06T23:59:00+00:00",
    "base_asset_bal": 1196.46,
    "quote_asset_bal": 8653.5184794233,
    "total_value_mid": 10106.0209194233,
    "total_value_exit_net": 10104.9315425933,
    "total_value": 10106.0209194233,
    "base_cost_quote": 1436.7913466205,
    "unrealized_pnl_exit_net": 14.6217165495
  },
  {
    "timestamp": "2023-01-07T23:59:00+00:00",
    "base_asset_bal": 1156.04,
    "quote_asset_bal": 8704.7840310863,
    "total_value_mid": 10110.5286710863,
    "total_value_exit_net": 10109.4743626063,
    "total_value": 10110.5286710863,
    "base_cost_quote": 1386.7104792194,
    "unrealized_pnl_exit_net": 17.9798523007
  },
  {
    "timestamp": "2023-01-08T23:59:00+00:00",
    "base_asset_bal": 920.88,
    "quote_asset_bal": 8998.9574983336,
    "total_value_mid": 10161.1080583336,
    "total_value_exit_net": 10160.2364454136,
    "total_value": 10161.1080583336,
    "base_cost_quote": 1116.3638182797,
    "unrealized_pnl_exit_net": 44.9151288003
  },
  {
    "timestamp": "2023-01-09T23:59:00+00:00",
    "base_asset_bal": 1141.22,
    "quote_asset_bal": 8717.8523116172,
    "total_value_mid": 10225.4039316172,
    "total_value_exit_net": 10224.2732679022,
    "total_value": 10225.4039316172,
    "base_cost_quote": 1497.0014187318,
    "unrealized_pnl_exit_net": 9.4195375532
  },
  {
    "timestamp": "2023-01-10T23:59:00+00:00",
    "base_asset_bal": 1014.62,
    "quote_asset_bal": 8895.6267239219,
    "total_value_mid": 10294.787703922,
    "total_value_exit_net": 10293.7383331869,
    "total_value": 10294.787703922,
    "base_cost_quote": 1376.8856215985,
    "unrealized_pnl_exit_net": 21.2259876665
  },
  {
    "timestamp": "2023-01-11T23:59:00+00:00",
    "base_asset_bal": 1185.67,
    "quote_asset_bal": 8669.9135181236,
    "total_value_mid": 10294.2814181236,
    "total_value_exit_net": 10293.0631421986,
    "total_value": 10294.2814181236,
    "base_cost_quote": 1607.2179277633,
    "unrealized_pnl_exit_net": 15.9316963117
  },
  {
    "timestamp": "2023-01-12T23:59:00+00:00",
    "base_asset_bal": 881.48,
    "quote_asset_bal": 9111.8933496299,
    "total_value_mid": 10372.4097496299,
    "total_value_exit_net": 10371.4643623299,
    "total_value": 10372.4097496299,
    "base_cost_quote": 1216.6628377684,
    "unrealized_pnl_exit_net": 42.9081749316
  },
  {
    "timestamp": "2023-01-13T23:59:00+00:00",
    "base_asset_bal": 928.92,
    "quote_asset_bal": 9053.5465185395,
    "total_value_mid": 10417.2010785396,
    "total_value_exit_net": 10416.1783376195,
    "total_value": 10417.2010785396,
    "base_cost_quote": 1316.7869107455,
    "unrealized_pnl_exit_net": 45.8449083345
  },
  {
    "timestamp": "2023-01-14T23:59:00+00:00",
    "base_asset_bal": 1259.56,
    "quote_asset_bal": 8560.2808142474,
    "total_value_mid": 10498.7436542474,
    "total_value_exit_net": 10497.2898071174,
    "total_value": 10498.7436542474,
    "base_cost_quote": 1912.6891740992,
    "unrealized_pnl_exit_net": 24.3198187708
  },
  {
    "timestamp": "2023-01-15T23:59:00+00:00",
    "base_asset_bal": 1408.73,
    "quote_asset_bal": 8357.1945215008,
    "total_value_mid": 10477.3331715008,
    "total_value_exit_net": 10475.7430675133,
    "total_value": 10477.3331715008,
    "base_cost_quote": 2137.9745731204,
    "unrealized_pnl_exit_net": -19.4260271079
  },
  {
    "timestamp": "2023-01-16T23:59:00+00:00",
    "base_asset_bal": 1568.69,
    "quote_asset_bal": 8141.0888265455,
    "total_value_mid": 10461.1813365455,
    "total_value_exit_net": 10459.441267163,
    "total_value": 10461.1813365455,
    "base_cost_quote": 2373.4513882754,
    "unrealized_pnl_exit_net": -55.0989476578
  },
  {
    "timestamp": "2023-01-17T23:59:00+00:00",
    "base_asset_bal": 2107.8,
    "quote_asset_bal": 7373.7320863647,
    "total_value_mid": 10347.8378863647,
    "total_value_exit_net": 10345.6073070147,
    "total_value": 10347.8378863647,
    "base_cost_quote": 3144.6224200906,
    "unrealized_pnl_exit_net": -172.7471994406
  },
  {
    "timestamp": "2023-01-18T23:59:00+00:00",
    "base_asset_bal": 3298.02,
    "quote_asset_bal": 5801.8247895058,
    "total_value_mid": 10029.8864295058,
    "total_value_exit_net": 10026.7153832758,
    "total_value": 10029.8864295058,
    "base_cost_quote": 4726.6961042042,
    "unrealized_pnl_exit_net": -501.8055104342
  },
  {
    "timestamp": "2023-01-19T23:59:00+00:00",
    "base_asset_bal": 3236.29,
    "quote_asset_bal": 5884.5498533792,
    "total_value_mid": 10146.7437833793,
    "total_value_exit_net": 10143.5471379318,
    "total_value": 10146.7437833793,
    "base_cost_quote": 4651.6061521466,
    "unrealized_pnl_exit_net": -392.6088675941
  },
  {
    "timestamp": "2023-01-20T23:59:00+00:00",
    "base_asset_bal": 607.06,
    "quote_asset_bal": 9837.5045890632,
    "total_value_mid": 10853.7230290632,
    "total_value_exit_net": 10852.9608652332,
    "total_value": 10853.7230290632,
    "base_cost_quote": 986.4836538592,
    "unrealized_pnl_exit_net": 28.9726223108
  },
  {
    "timestamp": "2023-01-21T23:59:00+00:00",
    "base_asset_bal": 1453.99,
    "quote_asset_bal": 8446.9856934605,
    "total_value_mid": 10796.6335334605,
    "total_value_exit_net": 10794.8712975805,
    "total_value": 10796.6335334605,
    "base_cost_quote": 2418.5359744739,
    "unrealized_pnl_exit_net": -70.6503703539
  },
  {
    "timestamp": "2023-01-22T23:59:00+00:00",
    "base_asset_bal": 1405.88,
    "quote_asset_bal": 8564.4467095364,
    "total_value_mid": 10895.3957495364,
    "total_value_exit_net": 10893.6475377564,
    "total_value": 10895.3957495364,
    "base_cost_quote": 2343.449878206,
    "unrealized_pnl_exit_net": -14.249049986
  },
  {
    "timestamp": "2023-01-23T23:59:00+00:00",
    "base_asset_bal": 1546.24,
    "quote_asset_bal": 8310.7524498979,
    "total_value_mid": 10999.6638098979,
    "total_value_exit_net": 10997.6471263779,
    "total_value": 10999.6638098979,
    "base_cost_quote": 2714.2249380963,
    "unrealized_pnl_exit_net": -27.3302616162
  },
  {
    "timestamp": "2023-01-24T23:59:00+00:00",
    "base_asset_bal": 3004.96,
    "quote_asset_bal": 5939.4085351449,
    "total_value_mid": 10573.0568551449,
    "total_value_exit_net": 10569.5816189049,
    "total_value": 10573.0568551449,
    "base_cost_quote": 5092.726723931,
    "unrealized_pnl_exit_net": -462.553640171
  },
  {
    "timestamp": "2023-01-25T23:59:00+00:00",
    "base_asset_bal": 1386.5,
    "quote_asset_bal": 8818.7696917393,
    "total_value_mid": 11490.5551917393,
    "total_value_exit_net": 11488.5513526143,
    "total_value": 11490.5551917393,
    "base_cost_quote": 2593.8793918802,
    "unrealized_pnl_exit_net": 75.9022689948
  },
  {
    "timestamp": "2023-01-26T23:59:00+00:00",
    "base_asset_bal": 853.16,
    "quote_asset_bal": 9931.0534701253,
    "total_value_mid": 11898.4404301253,
    "total_value_exit_net": 11896.9648899053,
    "total_value": 11898.4404301253,
    "base_cost_quote": 1747.9638832196,
    "unrealized_pnl_exit_net": 217.9475365604
  },
  {
    "timestamp": "2023-01-27T23:59:00+00:00",
    "base_asset_bal": 1012.2,
    "quote_asset_bal": 9615.6109633606,
    "total_value_mid": 12060.0739633606,
    "total_value_exit_net": 12058.2406161106,
    "total_value": 12060.0739633606,
    "base_cost_quote": 2188.6649974467,
    "unrealized_pnl_exit_net": 253.9646553033
  },
  {
    "timestamp": "2023-01-28T23:59:00+00:00",
    "base_asset_bal": 1477.23,
    "quote_asset_bal": 8550.3559850903,
    "total_value_mid": 11942.0760650903,
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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 (90,512 closed trades) via GET /backtests/f5b83fa5-668f-4498-adf6-94c6c288fa03/trades (paginate through all pages).
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
,,2023-01-01T00:04:00Z,2023-01-01T14:12:00Z,1.1091,1.13683,9.02,,,0.234670428,2.3483502389,0.0075030615,0.0076821287,,,TP
,,2023-01-01T00:04:00Z,2023-01-01T14:08:00Z,1.1091,1.11188,9.02,,,0.0100395276,0.1004656929,0.0075030615,0.0075135291,,,TP
,,2023-01-01T00:04:00Z,2023-01-05T07:08:00Z,1.1091,1.22002,13.53,,,1.4771128548,9.8434009557,0.0112545923,0.012380153,,,TP
,,2023-01-01T00:04:00Z,2023-01-02T07:14:00Z,1.1091,1.16456,9.02,,,0.4843303425,4.8467004779,0.0075030615,0.0078695142,,,TP
,,2023-01-01T00:14:00Z,2023-01-01T00:31:00Z,1.1051,1.10787,9.05,,,0.0100479661,0.1004680572,0.0075008663,0.0075196676,,,TP
,,2023-01-01T00:14:00Z,2023-01-01T14:12:00Z,1.1051,1.13273,9.05,,,0.2348622289,2.3483510542,0.0075008663,0.0076884049,,,TP
,,2023-01-01T00:14:00Z,2023-01-05T04:23:00Z,1.1051,1.21562,13.58,,,1.4772250668,9.8434042168,0.0112554435,0.0123810897,,,TP
,,2023-01-01T00:14:00Z,2023-01-01T22:38:00Z,1.1051,1.16036,9.05,,,0.4847261902,4.8467021084,0.0075008663,0.0078759435,,,TP
,,2023-01-01T00:37:00Z,2023-01-01T00:41:00Z,1.1031,1.10586,9.07,,,0.0100067496,0.1000163177,0.0075038378,0.0075226127,,,TP
,,2023-01-01T00:37:00Z,2023-01-01T14:12:00Z,1.1031,1.13068,9.07,,,0.2349553115,2.3483514641,0.0075038378,0.0076914507,,,TP
,,2023-01-01T00:37:00Z,2023-01-01T22:38:00Z,1.1031,1.15826,9.07,,,0.4849182986,4.8467029281,0.0075038378,0.0078790637,,,TP
,,2023-01-01T00:37:00Z,2023-01-05T04:23:00Z,1.1031,1.21342,13.6,,,1.476723496,9.8434058562,0.01125162,0.012376884,,,TP
,,2023-01-01T00:44:00Z,2023-01-01T14:06:00Z,1.1061,1.10887,9.05,,,0.0100343911,0.1002416147,0.0075076538,0.0075264551,,,TP
,,2023-01-01T00:49:00Z,2023-01-01T06:51:00Z,1.1041,1.10687,9.06,,,0.0100726589,0.1006949099,0.0075023595,0.0075211817,,,TP
,,2023-01-01T00:50:00Z,2023-01-01T07:05:00Z,1.1021,1.10486,9.08,,,0.0100314024,0.1002431721,0.007505301,0.0075240966,,,TP
,,2023-01-01T01:02:00Z,2023-01-01T07:05:00Z,1.10008,1.10284,9.1,,,0.010081071,0.1007026762,0.007508046,0.007526883,,,TP
,,2023-01-01T01:02:00Z,2023-01-02T21:26:00Z,1.10008,1.21009,13.64,,,1.4769033609,9.8426816686,0.0112538184,0.0123792207,,,TP
,,2023-01-01T01:02:00Z,2023-01-01T14:12:00Z,1.10008,1.12759,9.1,,,0.2351371523,2.3488516744,0.007508046,0.0076958018,,,TP
,,2023-01-01T01:02:00Z,2023-01-01T22:38:00Z,1.10008,1.15509,9.1,,,0.4851994648,4.8467950058,0.007508046,0.0078834893,,,TP
,,2023-01-01T01:16:00Z,2023-01-02T21:26:00Z,1.09709,1.2068,13.68,,,1.4771948886,9.8425910819,0.0112561434,0.012381768,,,TP
,,2023-01-01T01:16:00Z,2023-01-01T14:12:00Z,1.09709,1.15195,9.12,,,0.4849397664,4.8467509502,0.0075040956,0.007879338,,,TP
,,2023-01-01T01:16:00Z,2023-01-01T02:38:00Z,1.09709,1.09984,9.12,,,0.0100529988,0.1004751205,0.0075040956,0.0075229056,,,TP
,,2023-01-01T01:16:00Z,2023-01-01T14:12:00Z,1.09709,1.12452,9.12,,,0.2349657876,2.3483754751,0.0075040956,0.0076917168,,,TP
,,2023-01-01T01:24:00Z,2023-01-02T21:26:00Z,1.09408,1.20349,13.72,,,1.4774632047,9.8426826649,0.0112580832,0.0123839121,,,TP
,,2023-01-01T01:24:00Z,2023-01-01T14:12:00Z,1.09408,1.14879,9.15,,,0.4852048046,4.8467979947,0.007508124,0.0078835714,,,TP
,,2023-01-30T19:38:00Z,2023-01-30T19:38:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-02-01T07:04:00Z,2023-02-01T19:48:00Z,3.08007,3.38808,4.88,,,1.479415371,9.8425973273,0.0112730562,0.0124003728,,,TP
,,2023-02-01T20:03:00Z,2023-11-14T23:35:00Z,3.44744,3.79219,4.36,,,1.4794364099,9.8426739117,0.0112731288,0.0124004613,,,TP
,,2023-02-01T20:05:00Z,2023-11-14T23:35:00Z,3.45545,3.801,4.35,,,1.4794683319,9.8426445904,0.0112734056,0.0124007625,,,TP
,,2023-02-02T04:30:00Z,2023-11-14T22:44:00Z,3.38538,3.72392,4.44,,,1.479443631,9.8425590333,0.0112733154,0.0124006536,,,TP
,,2023-02-02T05:51:00Z,2023-11-15T00:07:00Z,3.6136,3.97497,4.16,,,1.4796228616,9.8427765248,0.011274432,0.0124019064,,,TP
,,2023-02-02T06:15:00Z,2023-11-14T23:38:00Z,3.53753,3.89129,4.25,,,1.4798006362,9.8426977298,0.0112758769,0.0124034869,,,TP
,,2023-02-02T06:36:00Z,2023-11-14T23:36:00Z,3.52051,3.87257,4.27,,,1.4796198613,9.842755453,0.0112744333,0.0124019054,,,TP
,,2023-02-03T15:19:00Z,2023-11-14T20:14:00Z,3.28227,3.6105,4.58,,,1.479616735,9.8425913316,0.0112745975,0.0124020675,,,TP
,,2023-02-04T04:36:00Z,2023-11-14T14:19:00Z,3.09909,3.409,4.85,,,1.4793903226,9.8425322433,0.0112729399,0.0124002375,,,TP
,,2023-02-11T07:15:00Z,2023-02-11T07:49:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-03-06T18:16:00Z,2023-03-06T18:24:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-03-23T13:55:00Z,2023-03-23T14:00:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-03-23T14:04:00Z,2023-03-23T14:13:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-03-28T12:15:00Z,2023-03-28T16:00:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-03-28T16:13:00Z,2023-03-28T16:18:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-03-28T16:47:00Z,2023-03-28T16:52:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-05-02T10:51:00Z,2023-05-02T12:41:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-05-06T09:57:00Z,2023-05-06T10:15:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-05-06T10:23:00Z,2023-05-06T10:39:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-05-06T12:06:00Z,2023-10-24T16:23:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-10-24T16:45:00Z,2023-10-24T16:45:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-10-26T08:06:00Z,2023-10-27T13:35:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-11-07T22:06:00Z,2023-11-07T22:08:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-11-08T01:54:00Z,2023-11-08T02:08:00Z,2.43943,2.44553,4.1,,,0.0099643852,0.0998708715,0.0075012473,0.0075016633,,,TP
,,2023-11-14T22:55:00Z,2023-11-15T05:56:00Z,3.70369,4.07406,4.06,,,1.4800189512,9.8425269798,0.0112777361,0.0124055127,,,TP
,,2023-11-14T23:40:00Z,2023-11-15T16:03:00Z,3.86385,4.25024,3.89,,,1.4793842424,9.8426293076,0.0112727824,0.0124000752,,,TP
,,2023-11-14T23:42:00Z,2023-11-15T16:03:00Z,3.85384,4.23923,3.9,,,1.4793487702,9.8426555721,0.011272482,0.0123997477,,,TP
,,2023-11-14T23:45:00Z,2023-11-15T16:03:00Z,3.84484,4.22933,3.91,,,1.4796783965,9.8426559363,0.0112749933,0.0124025102,,,TP
,,2023-11-14T23:46:00Z,2023-11-15T16:02:00Z,3.83583,4.21942,3.92,,,1.479990365,9.842682353,0.0112773402,0.0124050948,,,TP
,,2023-11-15T00:32:00Z,2023-11-15T16:12:00Z,3.91591,4.30751,3.84,,,1.4800605504,9.8427296593,0.0112778208,0.0124056288,,,TP
,,2023-11-15T00:50:00Z,2023-11-15T16:19:00Z,3.95695,4.35265,3.8,,,1.47997764,9.8426262652,0.0112773075,0.0124050525,,,TP
,,2023-11-15T00:55:00Z,2023-11-15T16:19:00Z,3.96596,4.36256,3.79,,,1.4794401819,9.8426007827,0.0112732413,0.0124005768,,,TP
,,2023-11-15T01:03:00Z,2023-11-15T16:12:00Z,3.94594,4.34054,3.81,,,1.4797473834,9.842651941,0.0112755236,0.0124030931,,,TP
,,2023-11-15T01:11:00Z,2023-11-15T16:02:00Z,3.87386,4.26125,3.88,,,1.4794000299,9.8426031787,0.0112729326,0.0124002375,,,TP
,,2023-11-15T01:50:00Z,2023-11-15T14:50:00Z,3.78778,4.16656,3.97,,,1.4800725527,9.8425527618,0.011278115,0.0124059324,,,TP
,,2023-11-15T16:07:00Z,2023-11-15T16:07:00Z,4.20319,4.2137,2.38,,,0.009947678,0.0998606416,0.0075026942,0.0074898518,,,TP
,,2023-11-15T16:14:00Z,2023-11-15T16:14:00Z,4.27527,4.28596,2.34,,,0.0099469506,0.0998551553,0.0075030989,0.0074897151,,,TP
,,2023-11-15T16:18:00Z,2023-11-15T16:18:00Z,4.27727,4.28797,2.34,,,0.0099632431,0.0999719447,0.0075066089,0.0074932276,,,TP
,,2023-11-15T16:19:00Z,2023-11-15T16:19:00Z,4.27826,4.28896,2.34,,,0.009959783,0.0999141006,0.0075083463,0.0074949576,,,TP
,,2023-11-15T16:46:00Z,2023-11-15T16:46:00Z,4.3143,4.32509,2.32,,,0.0099571568,0.0999109357,0.007506882,0.0074932184,,,TP
,,2023-11-15T16:47:00Z,2023-11-15T16:52:00Z,4.31531,4.3261,2.32,,,0.0099536572,0.099852444,0.0075086394,0.0074949683,,,TP
,,2023-11-15T16:55:00Z,2023-11-15T16:56:00Z,4.27727,4.28797,2.34,,,0.0099632431,0.0999719447,0.0075066089,0.0074932276,,,TP
,,2023-11-15T17:01:00Z,2023-11-15T17:01:00Z,4.31531,4.3261,2.32,,,0.0099536572,0.099852444,0.0075086394,0.0074949683,,,TP
,,2023-11-15T17:07:00Z,2023-11-15T17:07:00Z,4.20319,4.2137,2.38,,,0.009947678,0.0998606416,0.0075026942,0.0074898518,,,TP
,,2023-11-17T11:53:00Z,2023-11-24T09:20:00Z,3.37837,3.71621,4.45,,,1.4797098392,9.8425887336,0.0112753099,0.0124028509,,,TP
,,2023-11-17T12:39:00Z,2023-11-25T01:39:00Z,3.38538,3.72392,4.44,,,1.479443631,9.8425590333,0.0112733154,0.0124006536,,,TP
,,2023-11-17T14:46:00Z,2023-11-24T08:19:00Z,3.32532,3.65786,4.52,,,1.4794078198,9.8427403979,0.0112728348,0.0124001454,,,TP
,,2023-11-17T17:39:00Z,2023-11-24T09:20:00Z,3.37036,3.7074,4.46,,,1.4795232928,9.8426185927,0.0112738542,0.012401253,,,TP
,,2023-11-20T05:38:00Z,2023-11-22T14:01:00Z,3.19118,3.5103,4.71,,,1.4793822219,9.8425626257,0.0112728434,0.0124001348,,,TP
,,2023-12-31T20:08:00Z,2023-12-31T21:19:00Z,3.03503,3.04262,3.3,,,0.0100048163,0.0998923404,0.0075116993,0.0075304845,,,TP
,,2023-12-31T20:12:00Z,2023-12-31T21:15:00Z,3.02902,3.0366,3.31,,,0.0100318983,0.1000582697,0.0075195422,0.0075383595,,,TP
,,2023-12-31T20:27:00Z,2023-12-31T21:52:00Z,3.02301,3.03057,3.31,,,0.0099955877,0.0998943106,0.0075046223,0.00752339,,,TP
,,2023-12-31T20:32:00Z,2023-12-31T21:08:00Z,3.01,3.01753,3.33,,,0.0100211438,0.0999784884,0.007517475,0.0075362812,,,TP
,,2023-12-31T20:36:00Z,2023-12-31T21:03:00Z,2.99999,3.00749,3.34,,,0.0100012626,0.0998133327,0.007514975,0.0075337625,,,TP
,,2023-12-31T21:19:00Z,2023-12-31T21:43:00Z,3.04103,3.04864,3.29,,,0.0100106393,0.1000564776,0.0075037415,0.0075225192,,,TP
,,2023-12-31T21:25:00Z,2023-12-31T21:30:00Z,3.03703,3.04463,3.3,,,0.0100278915,0.1000567989,0.0075166493,0.0075354593,,,TP
,,2023-12-31T21:39:00Z,2023-12-31T21:45:00Z,3.03703,3.04463,3.3,,,0.0100278915,0.1000567989,0.0075166493,0.0075354593,,,TP
,,2023-12-31T21:52:00Z,2023-12-31T22:05:00Z,3.03002,3.0376,3.31,,,0.0100269333,0.0999757427,0.0075220247,0.007540842,,,TP
,,2023-12-31T22:16:00Z,2023-12-31T22:20:00Z,3.03102,3.0386,3.3,,,0.0099916905,0.0998932703,0.0075017745,0.007520535,,,TP
,,2023-12-31T22:45:00Z,2023-12-31T22:46:00Z,2.97296,2.9804,3.37,,,0.0100256826,0.1000679458,0.0075141564,0.007532961,,,TP
,,2023-12-31T22:51:00Z,2023-12-31T22:53:00Z,2.9129,2.92019,3.44,,,0.0100282278,0.1000783583,0.007515282,0.0075340902,,,TP
,,2023-12-31T22:51:00Z,2023-12-31T22:51:00Z,2.93793,2.94528,3.41,,,0.0100171904,0.0999885123,0.007513756,0.0075325536,,,TP
,,2023-12-31T22:55:00Z,2023-12-31T22:55:00Z,2.93192,2.93925,3.42,,,0.010009049,0.0998193164,0.0075203748,0.0075391763,,,TP
,,2023-12-31T22:56:00Z,2023-12-31T23:00:00Z,2.93393,2.94127,3.41,,,0.010003576,0.0999887523,0.007503526,0.007522298,,,TP
,,2023-12-31T23:01:00Z,2023-12-31T23:02:00Z,2.93893,2.94628,3.41,,,0.0100120754,0.0999034513,0.0075163135,0.0075351111,,,TP
,,2023-12-31T23:04:00Z,2023-12-31T23:05:00Z,2.95395,2.96134,3.39,,,0.0100124752,0.0999858664,0.0075104179,0.007529207,,,TP
,,2023-12-31T23:17:00Z,2023-12-31T23:50:00Z,2.93393,2.94127,3.41,,,0.010003576,0.0999887523,0.007503526,0.007522298,,,TP
,,2023-12-31T23:19:00Z,2023-12-31T23:45:00Z,2.92692,2.93424,3.42,,,0.0100005246,0.099904678,0.0075075498,0.0075263256,,,TP
,,2023-12-31T23:20:00Z,2023-12-31T23:48:00Z,2.93092,2.93825,3.42,,,0.0100141789,0.0999045522,0.0075178098,0.0075366112,,,TP
,,2023-12-31T23:22:00Z,2023-12-31T23:42:00Z,2.92191,2.92922,3.43,,,0.0100212681,0.0999911873,0.0075166135,0.0075354185,,,TP
,,2023-12-31T23:24:00Z,2023-12-31T23:35:00Z,2.91591,2.9232,3.43,,,0.0099835895,0.0998202105,0.0075011785,0.007519932,,,TP
,,2023-12-31T23:26:00Z,2023-12-31T23:33:00Z,2.9129,2.92019,3.44,,,0.0100282278,0.1000783583,0.007515282,0.0075340902,,,TP
,,2023-12-31T23:31:00Z,2023-12-31T23:33:00Z,2.9099,2.91718,3.44,,,0.0100093336,0.0999927833,0.007507542,0.0075263244,,,TP
,,2023-12-31T23:35:00Z,2023-12-31T23:37:00Z,2.91691,2.92421,3.43,,,0.0100127188,0.1000771364,0.007503751,0.0075225302,,,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”.
The FullBullMarket strategy was tested on DYDXUSDT over 2023-01-01 to 2023-12-31.Source: bt.mode_name, bt.symbol, bt.config.from, bt.config.to
Completed. Final metrics are available below.Source: bt.status, bt.error_message
+107.37% - turning 10,000.00 USDT into 20,736.74 USDT (net profit 12,279.13 USDT).Source: bt.return_pct, bt.start_balance, bt.final_value, bt.total_profit
Compound Annual Growth Rate is +107.89% over a 364 day window.Source: derived from equity series (eqStats.cagrPct, eqStats.durationDays)
No - Buy & Hold returned +167.45%, beating the strategy by +60.09%.Source: bt.return_pct, opts.tokenReturnPct, outperformDelta
The maximum peak-to-trough loss was -36.46% (5,934.91 USDT), lasting around 57 days underwater and taking 132 days to recover.Source: derived from equity series (eqStats.maxDrawdownPct, .maxDrawdownAbs, .underwaterDays, .recoveryDays)
90,512 trades were filled with a 100.0% win rate (~7547.8 trades per month). 965 positions were still open at the end of the window.Source: bt.fulfilled_trades, bt.win_rate, bt.active_orders, derived tradesPerMonth
Average trade profit was 0.14 USDT, the best single trade made 1.48 USDT, the worst single trade still finished positive at +0.01 USDT (no closed trade ended in a loss).Source: bt.avg_profit, bt.max_profit, bt.min_profit
Profit factor Infinity (gross profit / gross loss) and expectancy 0.51 USDT per trade. Payoff ratio (avg win / avg loss) is Infinity.Source: derived from trades (stats.profitFactor, .expectancy, .payoffRatio)
Longest winning streak: 100 trades. Longest losing streak: 0 trades.Source: derived from trades (stats.consecWinsMax, .consecLossesMax)
Total fees were 1.77 USDT (buy: 0.87, sell: 0.91) 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)
Annualized Sharpe is 1.47 and Sortino is 1.53. 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)
Annualized volatility of daily returns was 63.35%.Source: derived from equity series (eqStats.volatilityAnnualizedPct)
The median hold time is 12.7 hours (mean 28.3 days, range 1.0 minutes - 286.1 days).Source: derived from trades (stats.medianHoldingHours, .avgHoldingHours, .minHoldingHours, .maxHoldingHours)
The strategy was in the market 100.0% of the period - the remainder sat in cash awaiting setups.Source: derived from equity series (eqStats.timeInMarketPct)
Best month: 2023-11 with 35.62 USDT. Worst month: 2023-05 with 0.03 USDT.Source: derived from trades (stats.bestMonth, .worstMonth)
Created at 2026-04-18T10:03:54.276911Z, started at 2026-04-19T11:15:50.002403Z, completed at 2026-04-19T11:56:06.602896Z. Engine elapsed time: 2250.64 s.Source: bt.created_at, bt.started_at, bt.completed_at, bt.elapsed_sec
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
Engine summary: Backtest DYDXUSDT (Mode: 1FullBullMarket.json)
Period: 2023-01-01 00:00:01 to 2023-12-31 23:59:59
Starting balance: 10,000.00 USDT
Final value: 20,736.74 USDT
P&L: +10,736.74 USDT (+107.37%)
Result: PROFIT
Completed trades: 90512
Open orders at end: 965
Win rate: 100.0%
Avg. profit/trade: 0.135663 USDT
Best trade: 1.480073 USDT
Worst trade: 0.009947 USDT
Total profit (trades only): 12,279.126502 USDT

Strategy parameters:
  Buy trigger: -0.1% from last buy
  Buy splits: 6
  Sell targets: [0.25, 2.5, 5.0, 10.0, 20.0, 30.0]
  Investment per buy: 50.0 USDT
  Fees: maker 7.5 bps / taker 7.5 bps

Elapsed: 2250.6s
Source: bt.summary_text
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.
TermDefinition
Return %Total percentage change of portfolio value over the backtest window: (final_value - start_balance) / start_balance x 100.
CAGRCompound Annual Growth Rate. Annualized return assuming compounding: (final/start)^(365.25/days) - 1.
Win RateShare of closed trades with positive net P&L (after fees): wins / (wins + losses) x 100.
Max DrawdownLargest peak-to-trough decline of equity, measured in % of the previous peak. The single most important risk metric.
Drawdown DurationNumber 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 MarketFraction of the period where the strategy held base-asset exposure (not 100% cash).
Buy & HoldReference 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 TradesRound-trip trades that have both an entry fill and an exit fill within the window.
Active OrdersOpen positions still pending exit at the end of the backtest - their unrealized P&L is included in final_value.
Lookahead BiasUsing 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 +107.37% over the tested window. Compare against Buy & Hold to judge true edge.
2

Validate trade frequency

90,512 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 -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":"DYDXUSDT","mode_name":"1FullBullMarket.json","interval":"1m","from":"2023-01-01 00:00:01","to":"2023-12-31 23:59:59","canBuy":true,"canSell":true,"canBuyUp":true,"startBal":10000,"stepSize":0.01,"stopLoss":false,"tickSize":0.00001,"buySplits":6,"buyVolumes":[20,20,20,30,5,5],"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":[0.25,2.5,5,10,20,30],"triggerCoolDown":1,"investmentPerBuy":50,"assumed_spread_bps":0,"stopLossPercentage":5,"investmentPercentMode":false,"minInvestmentPerQuote":20,"order_latency_seconds":2,"trailingStopLossPercentages":[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 37 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": "DYDXUSDT FullBullMarket Backtest",
  "alternateName": "DYDXUSDT-1FullBullMarket.json-f5b83fa5-668f-4498-adf6-94c6c288fa03",
  "description": "Backtest of the FullBullMarket strategy on DYDXUSDT: +107.37% return over 2023-01-01 to 2023-12-31.",
  "identifier": "f5b83fa5-668f-4498-adf6-94c6c288fa03",
  "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": [
    "DYDXUSDT",
    "FullBullMarket",
    "crypto backtest",
    "trading strategy",
    "quantitative finance",
    "algorithmic trading"
  ],
  "about": [
    {
      "@type": "Thing",
      "name": "DYDXUSDT",
      "description": "Trading pair: DYDXUSDT"
    },
    {
      "@type": "Thing",
      "name": "FullBullMarket",
      "description": "Trading strategy: FullBullMarket"
    }
  ],
  "measurementTechnique": "Historical OHLCV replay with deterministic order-fill simulation (TP / SL / Trailing-SL)",
  "variableMeasured": [
    {
      "@type": "PropertyValue",
      "name": "Return",
      "value": 107.3674,
      "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": 12279.12650165,
      "unitText": "USDT",
      "description": "Total realized profit in quote currency"
    },
    {
      "@type": "PropertyValue",
      "name": "Final Portfolio Value",
      "value": 20736.7447252,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Initial Capital",
      "value": 10000,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Fulfilled Trades",
      "value": 90512,
      "unitText": "trades",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Active Orders",
      "value": 965,
      "unitText": "orders",
      "description": "Open positions still pending exit at end of window",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Best Trade Profit",
      "value": 1.48007255,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Worst Trade Profit",
      "value": 0.00994695,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Average Trade Profit",
      "value": 0.13566297,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Engine Elapsed Time",
      "value": 2250.64,
      "unitText": "seconds",
      "unitCode": "SEC",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "CAGR",
      "value": 107.88745830717272,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Compound Annual Growth Rate"
    },
    {
      "@type": "PropertyValue",
      "name": "Max Drawdown",
      "value": -36.460696786160064,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Largest peak-to-trough decline of equity (negative)",
      "maxValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Max Drawdown (absolute)",
      "value": -5934.910867834,
      "unitText": "USDT",
      "maxValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Drawdown Duration",
      "value": 57,
      "unitText": "days",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Recovery Duration",
      "value": 132,
      "unitText": "days",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Sharpe Ratio (annualized)",
      "value": 1.4704851780561925,
      "description": "Mean / std of daily returns x 365"
    },
    {
      "@type": "PropertyValue",
      "name": "Sortino Ratio (annualized)",
      "value": 1.5278972575717429,
      "description": "Like Sharpe but only penalizes downside volatility"
    },
    {
      "@type": "PropertyValue",
      "name": "Volatility (annualized)",
      "value": 63.34716414624639,
      "unitText": "%",
      "unitCode": "P1",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Time in Market",
      "value": 100,
      "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": 0.5051664410020004,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Average Win",
      "value": 0.5051664410020004,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Average Loss",
      "value": 0,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Median Trade Profit",
      "value": 0.010029815099999999,
      "unitText": "USDT"
    },
    {
      "@type": "PropertyValue",
      "name": "Gross Profit",
      "value": 50.51664410020004,
      "unitText": "USDT",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Gross Loss",
      "value": 0,
      "unitText": "USDT",
      "maxValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Total Fees Paid",
      "value": 1.7733046344000003,
      "unitText": "USDT",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Total Volume Traded",
      "value": 2365.2171477,
      "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": 12.733333333333333,
      "unitText": "hours",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Mean Holding Time",
      "value": 679.9352059925096,
      "unitText": "hours",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Trades per Month",
      "value": 7547.8333553927805,
      "unitText": "trades/month",
      "minValue": 0
    },
    {
      "@type": "PropertyValue",
      "name": "Buy & Hold Return",
      "value": 167.45240253853132,
      "unitText": "%",
      "unitCode": "P1",
      "description": "Reference: token price change over the same window"
    },
    {
      "@type": "PropertyValue",
      "name": "Outperformance vs Buy & Hold",
      "value": -60.08500253853131,
      "unitText": "pp",
      "description": "Strategy return minus Buy & Hold return, in percentage points"
    }
  ],
  "dateCreated": "2026-04-18T10:03:54.276Z",
  "dateModified": "2026-05-15T11:42:23.858Z",
  "temporalCoverage": "2022-12-31T23:00:01.000Z/2023-12-31T22:59:59.000Z"
}

​
[!] 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 11:42:23 UTC
Last modified on May 15, 2026