Build a quantile strategy

Create an asynchronous forecast, test executable research rules and retain the evidence needed to reproduce them.

Build a quantile strategy

Quantura separates forecasting from execution. A forecast supplies time-stamped quantiles; a strategy specifies a decision rule, position sizing, fill assumptions and risk limits. The API and MCP documentation do not authorize broker orders.

1. Freeze a source and interval

Use Search to select the exact provider instrument or market outcome. Use Download to inspect the observations and price units. Retain the source, quote side, adjustment, timezone, interval and cutoff with the exported snapshot.

Examples: EURUSD from Dukascopy is a provider FX quote; a Dukascopy equity CFD is not an exchange share; a Kalshi game contract is a decimal probability. Distances such as one dollar, ten pips and ten index points cannot be interchanged.

2. Request and await an ensemble

{
  "source":{"type":"ticker","provider":"dukascopy","symbol":"EURUSD","frequency":"1Hour","price_side":"bid","limit":500},
  "prediction_length":23,
  "horizon_mode":"frequency_periods",
  "calendar":"NONE",
  "quantiles":[0.01,0.25,0.5,0.75,0.9,0.99],
  "transform":"log",
  "failure_policy":"fail",
  "models":{
    "prophet":{"enabled":true,"weight":1},
    "chronos":{"enabled":true,"weight":1}
  }
}

Submit this body to POST /api/v1/ensemble-forecasts with your authorized session/API key and an Idempotency-Key. Poll the returned status URL until completed or failed. Persist the configuration, timestamps, result hash and effective weights before evaluating a signal. Select additional available models from GET /api/v1/forecast/models; all requested tail quantiles must have genuine model support.

For a daily 18:00–17:00 UTC session, 23 hourly steps start after the last actual input. If its cutoff is earlier than 18:00 because of missing observations, request enough periods to bridge that gap or use prediction_end_at for the next 17:00 UTC boundary. Start taking decisions only after inference actually completes; a backdated cutoff does not make the forecast available in the past.

3. Run the supported public rule builder

POST /api/v1/backtests provides a bounded long-only, one-position quantile-rule replay. This valid example enters below P01 and exits at P50:

{
  "source":{"type":"ticker","symbol":"SPY","provider":"auto","frequency":"1Hour"},
  "forecast":{"prediction_length":6,"quantiles":[0.01,0.5],"models":{"prophet":{"enabled":true,"weight":1}},"failure_policy":"fail"},
  "replay":{"context_rows":128,"evaluation_windows":2},
  "strategy":{"schema_version":2,"type":"quantile_rules","entry_logic":"all","rules":[
    {"id":"buy_below_p01","kind":"entry","condition":"at_or_below","quantile":0.01},
    {"id":"take_p50","kind":"take_profit","target_mode":"quantile","quantile":0.5}
  ]},
  "execution":{"starting_capital":1000,"position_fraction":0.1,"commission_bps":10,"slippage_bps":5}
}

This example illustrates the public schema. It does not reproduce the year-long averaged basket study: the public builder has no short ladders, basket-average targets, broker lots, swap engine or median-direction filter. Inspect GET /api/v1/backtests/strategy-schema for supported fields; unsupported rules are rejected.

Signals use completed closes and fill at the next observed bar open. Read the completed result and export GET /api/v1/backtests/{id}/strategy. The export remains live_eligible: false. See Backtesting for limits and fill assumptions.

4. Reproduce the averaged-basket research

The repository’s dedicated research workers implement the separate SPY and FTMO-style studies. FTMO hourly methodology documents the matrix workflow, frozen costs and source limitations.

Rule	Long ladder	Short ladder
Initial signal	Observed price below an available P01	Observed price above an available P99
Averaging direction	Lower only	Higher only
Each research leg	0.01 lot	0.01 lot
Spacing and basket target	Instrument-specific distance	Same instrument-specific distance
Exit quote	Bid at least average ask entry + distance	Ask at most average bid entry − distance
Carry	Until target; mark any open liability at the cutoff	Same

The tested starting distances are 10 pips for FX and 10 points for indices; gold/BTC studies use separate dollar distances. Long and short statistics remain separate. Maintain volume/margin checks and record every leg. A basket hitting a gross target can still lose after commissions and swaps.

The triggered variant detects a primary P01/P99 breach from real minute observations, runs a second forecast using the latest 500 completed H1 observations through the remaining 17:00 UTC horizon, and waits for the refreshed P01/P99. New ladder additions start only on a full hourly bar after measured inference. Carrying exits stay active after the forecast window; there are no additions without a valid refreshed forecast.

on a genuinely completed observation:
    reject missing, stale or not-yet-published forecast rows
    calculate equity including spread, commissions, swaps and open legs
    test the executable basket target before considering a new addition
    if a primary P01/P99 breach occurs, request the causal second forecast
    wait for its completion and the next full actionable hourly bar
    add only on the required side of its quantile and beyond spacing/padding
    retain one addition per observed hour and enforce margin/volume constraints
    archive the decision, observed quote, forecast ID, costs and basket state

Start the repository research workflow

The original yearly matrix can be run from GitHub Actions or the authenticated GitHub CLI. This example limits the matrix to EURUSD and pins the release code:

gh workflow run ftmo-dukas-year.yml \
  --repo tamzid2001/stockssagemakerdata --ref v2.1.0 \
  -f study_mode=original -f symbols=EURUSD.sim \
  -f start_date=2025-09-26 -f end_date=2026-09-25
gh run list --repo tamzid2001/stockssagemakerdata \
  --workflow ftmo-dukas-year.yml --limit 5

This runs real research models and downloads; the repository operator must provision approved checkpoint access/licensing and artifact secrets. Source, forecast, replay and report jobs form a matrix. Download the completed report artifacts before retention expires. The triggered workflow requires a completed parent yearly run with its frozen plan/source/forecast artifacts still available. Inspect that run’s exact artifacts rather than relying on an expired default run ID.

5. Compare losses and drawdown honestly

Evaluate basket win rate, entry-leg win rate, losing-session rate, realized profit, open P&L, drawdown, maximum ladder, time in market, margin and cost sensitivity together. Carry-until-target can produce a high closed-basket win rate while leaving losing positions open.

Freeze entry padding, median-direction filters and ladder caps on the earlier development period. Evaluate the chosen settings on a later period starting flat; keep a baseline and both possible hourly OHLC orders. Report failed/missing forecast days and current-cost assumptions.

The research comparison uses a $100,000 account with a static$90,000 equity floor and a $5,000 daily-loss diagnostic referenced to midnight account balance in Europe/Prague. The account-limit definitions follow the FTMO 2-Step trading objectives. These are diagnostic checks, not a demonstrated challenge pass or live loss-prevention system. Broker specifications, minimum executable volumes and historical swaps must be verified for the actual account before any execution design.

6. Use the HTTP API and MCP for their documented roles

Forecast/backtest creation uses the authenticated HTTP API with forecasts:write or backtests:run and current workspace permission. Read endpoints recheck access. MCP documentation search and the read-only forecast-capabilities tool help discover supported frequencies/models; they do not start jobs or place orders.

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