Trading Models API

Use this endpoint to retrieve the currently dominant Trading Models for a symbol.
A Trading Model is the combination of Entry Logic (entry generation), Position Manager (risk & exits), and Regime Filter (when to trade).

Endpoint

GET https://api.darwintiq.com/v1/models

Query parameters

symbol (required) — e.g. EURUSD, USDJPY, XAUUSD, SP500, DAX
sort (optional) — fitness (default) | latest | entryMove
limit (optional) — number of Trading Models to return (default: 5, max: 50)
entryType (optional) — filter to a specific Entry Logic type

Example

curl -H "Authorization: Bearer [YOUR_TOKEN]" \
  "https://api.darwintiq.com/v1/models?symbol=EURUSD&sort=fitness"

Response (example)

{
  "status": "success",
  "symbol": "EURUSD",
  "trading_models": [
    {
      "id": "abc123",
      "timeframe": "H1",
      "created": "2026-02-10 09:20:00",
      "tested": "2026-02-10 13:20:00",

      "fitness": 74.6,
      "stabilityScore": 72,
      "entryMovePips": -6.4,
      "entryPrice": 1.08742,
      "currentPrice": 1.08678,

      "entryLogic": {
        "type": "breakout_extrema",
        "class": "BreakoutExtrema",
        "description": "Breakout entries around recent extrema.",
        "params": {}
      },
      "positionManager": {
        "type": "absolute",
        "class": "Absolute",
        "description": "Fixed stop and trailing stop.",
        "params": {},
        "initialStopPips": 20,
        "trailingStopPips": 10,
        "takeProfitPips": 40
      },
      "regimeFilter": {
        "type": "nofilter",
        "class": "Nofilter",
        "description": "No regime filter applied.",
        "params": {}
      },

      "lastEntry": {
        "type": "BUY",
        "time": "2026-02-10 10:00:00",
        "timeElapsed": "20 minutes ago"
      },

      "totalPips": 132,
      "pipsPerTrade": 3.1,
      "tradesWon": 14,
      "tradesLost": 7,
      "pipsWon": 196,
      "pipsLost": 64,
      "maxDrawdown": 12.4,
      "expectedValue": 0.62,
      "profitFactor": 1.74,
      "sharpe": 0.38,
      "sortino": 0.56,
      "calmar": 1.92,
      "avgDrawdown": 4.7,
      "exposure": 0.41,
      "returnStability": 0.78,
      "testPeriod": { "from": "2026-02-10 09:20:00", "to": "2026-02-10 13:20:00" }
    }
  ],
  "timeSemantics": {
    "version": 1,
    "fieldBases": {
      "trading_models[].lastEntry.time": "BROKER_NY_CLOSE",
      "trading_models[].lastEntry.timeElapsed": "RELATIVE",
      "trading_models[].testPeriod.from": "BROKER_NY_CLOSE",
      "trading_models[].testPeriod.to": "BROKER_NY_CLOSE",
      "trading_models[].created": "RELATIVE",
      "trading_models[].tested": "RELATIVE"
    }
  }
}

Field reference

Field	Type	Description
`status`	string	`success` when the request succeeds.
`symbol`	string	Requested symbol (e.g., `EURUSD`).
`trading_models`	array	List of trading model objects.
`trading_models[].id`	string	Stable model identifier.
`trading_models[].timeframe`	string	Model timeframe.
`trading_models[].created`	string	Humanized relative creation time label (for example `2h ago`).
`trading_models[].tested`	string	Humanized relative backtest time label (for example `15m ago`).
`trading_models[].fitness`	number	Fitness score (higher = better).
`trading_models[].stabilityScore`	number \| null	Drift-based stability score (0–100).
`trading_models[].entryMovePips`	number \| null	Signed pip move since last entry.
`trading_models[].entryPrice`	number \| null	Entry price of last signal.
`trading_models[].currentPrice`	number \| null	Current price used for move calculation.
`trading_models[].entryLogic`	object	Structured entry logic block.
`trading_models[].positionManager`	object	Structured position manager block.
`trading_models[].regimeFilter`	object	Structured regime filter block.
`trading_models[].lastEntry`	object	Last entry time and direction.
`trading_models[].lastEntry.time`	string	Last entry timestamp in broker chart time (`BROKER_NY_CLOSE`).
`trading_models[].lastEntry.timeElapsed`	string	Humanized relative time label (`RELATIVE`).
`trading_models[].totalPips`	number	Total net pips in test window.
`trading_models[].pipsPerTrade`	number	Average pips per trade.
`trading_models[].tradesWon`	number	Number of winning trades.
`trading_models[].tradesLost`	number	Number of losing trades.
`trading_models[].pipsWon`	number	Total pips from wins.
`trading_models[].pipsLost`	number	Total pips from losses.
`trading_models[].maxDrawdown`	number	Max drawdown (pips).
`trading_models[].expectedValue`	number	Expected value (pips).
`trading_models[].profitFactor`	number \| null	Gross wins divided by gross losses.
`trading_models[].sharpe`	number \| null	Return/volatility ratio (trade-return based).
`trading_models[].sortino`	number \| null	Return/downside-volatility ratio.
`trading_models[].calmar`	number \| null	Net return divided by max equity drawdown.
`trading_models[].avgDrawdown`	number	Average drawdown (pips).
`trading_models[].exposure`	number	Time in market ratio (0..1).
`trading_models[].returnStability`	number \| null	Smoothness of equity trend (R², 0..1).
`trading_models[].testPeriod`	object	Backtest window (`from`/`to`).
`trading_models[].testPeriod.from`	string	Backtest window start in broker chart time (`BROKER_NY_CLOSE`).
`trading_models[].testPeriod.to`	string	Backtest window end in broker chart time (`BROKER_NY_CLOSE`).
`trading_models[].analytics`	object	Personality + stability analytics.
`trading_models[].analytics.stability`	object \| null	Stability metrics (stdDev, drift, MI).
`trading_models[].analytics.stability.score`	number \| null	Stability score (0–100), derived from drift metrics.
`trading_models[].analytics.stability.stdDev`	number \| null	Standard deviation of trade outcomes (pips).
`trading_models[].analytics.stability.drift`	object \| null	Drift metrics across features.
`trading_models[].analytics.stability.drift.profit_pips`	object \| null	Drift on profit per trade.
`trading_models[].analytics.stability.drift.rr`	object \| null	Drift on reward-to-risk distribution.
`trading_models[].analytics.stability.drift.holding_time`	object \| null	Drift on holding time distribution.
`trading_models[].analytics.stability.drift.stop_loss_pips`	object \| null	Drift on stop-loss sizing.
`trading_models[].analytics.stability.drift.*.js`	number \| null	Jensen–Shannon divergence.
`trading_models[].analytics.stability.drift.*.wasserstein`	number \| null	Wasserstein distance.
`trading_models[].analytics.stability.drift.*.ks`	number \| null	KS statistic.
`trading_models[].analytics.stability.drift.*.psi`	number \| null	Population Stability Index.
`trading_models[].analytics.stability.mi`	object \| null	Mutual information metrics.
`trading_models[].analytics.stability.mi.winloss_profit_pips`	number \| null	MI between win/loss and profit distribution.
`trading_models[].analytics.stability.mi.winloss_holding_time`	number \| null	MI between win/loss and holding time.
`trading_models[].analytics.stability.mi.winloss_rr`	number \| null	MI between win/loss and reward-to-risk.
`trading_models[].analytics.stability.mi.winloss_stop_loss_pips`	number \| null	MI between win/loss and stop-loss sizing.
`trading_models[].analytics.stability.driftWindow`	object \| null	Drift window meta.
`trading_models[].analytics.stability.driftWindow.split`	string \| null	Split strategy (e.g., `first_half_vs_second_half`).
`trading_models[].analytics.stability.driftWindow.start`	string \| null	Backtest start time.
`trading_models[].analytics.stability.driftWindow.mid`	string \| null	Midpoint time used for split.
`trading_models[].analytics.stability.driftWindow.end`	string \| null	Backtest end time.
`trading_models[].analytics.stability.driftWindow.min_trades_per_half`	number \| null	Minimum trades per half for drift calc.
`trading_models[].analytics.stability.driftWindow.insufficient_trades`	boolean \| null	True if drift couldn’t be computed.
`trading_models[].analytics.stability.driftWindow.pre_trades`	number \| null	Trades in first half.
`trading_models[].analytics.stability.driftWindow.post_trades`	number \| null	Trades in second half.
`timeSemantics`	object \| null	Optional timestamp semantics metadata for interpreting timestamp fields.
`timeSemantics.version`	number	Metadata version (currently `1`).
`timeSemantics.fieldBases`	object	Map of field paths to time basis identifiers.
`timeSemantics.notes`	array \| null	Optional human-readable notes about timestamp semantics.

Notes on the core ranking fields

Fitness: overall score for recent performance + consistency.
Stability Score: drift-based stability estimate (higher = more stable).
Entry Move (pips): signed pip movement since the last entry, measured at request time.
Entry/Current Price: raw prices used for the move calculation, useful for UI context.
Relative labels like created, tested, and lastEntry.timeElapsed are display strings (RELATIVE) and should not be parsed as absolute timestamps.

Timestamp semantics

BROKER_NY_CLOSE means Darwinex/MT4 chart time aligned to New York close (typically UTC+2 in winter / UTC+3 in summer).
RELATIVE means a human-readable label (for example 5 minutes ago), not an absolute timestamp.
If timeSemantics is present, prefer it over hard-coded assumptions in API clients.

Stability metrics

Std Dev: dispersion of trade outcomes within the backtest window.
Drift (JS / Wasserstein / KS / PSI): distribution shift between the first and second half of the window.
Mutual Information (MI): how informative the distribution is about win/loss outcomes.
Profit Factor / Sharpe / Sortino / Calmar: risk-adjusted performance and drawdown efficiency metrics.
Avg Drawdown / Exposure / Return Stability: typical drawdown pressure, time-in-market, and equity smoothness.

Errors

400 invalid symbol or query
401 missing/invalid token
429 rate limited
500 internal error