tradedesk

tradedesk is an event-driven trading framework for building, running, and evaluating systematic strategies across backtesting and live broker environments.

What it provides

• Event-driven strategy execution
• Shared strategy model across backtest and live runs
• Market data aggregation and indicators
• External market datasets and parsers
• Portfolio orchestration and risk controls
• Trade recording, metrics, and reporting
• Research helpers for walk-forward validation and correlation checks
• Optional machine-learning helpers in tradedesk.ml

The design goal is portability: strategies react to framework events rather than broker-specific implementations, so the same strategy code can move between backtest and live execution with minimal runtime wiring changes.

Installation

Python 3.11+ is required.

Install the published package:

pip install tradedesk

Install the optional machine-learning dependencies:

pip install 'tradedesk[ml]'

For local development:

pip install -e '.[dev]'

Architecture at a glance

The public package is organized into a small set of domains:

• tradedesk.marketdata for market events, subscriptions, aggregation, and indicators
• tradedesk.data_sources for external datasets such as CFTC COT history
• tradedesk.strategy for strategy base classes and strategy-facing events
• tradedesk.portfolio for portfolio state, sizing, and risk policies
• tradedesk.execution for live execution adapters and order handling
• tradedesk.execution.backtest for simulated execution and replay
• tradedesk.recording for lifecycle events, trade records, and metrics
• tradedesk.research for walk-forward and correlation-gate helpers
• tradedesk.ml for optional feature engineering, labels, and walk-forward tooling

For a broader system map, see docs/architecture.md.

Runtime model

Typical flow:

1. Market data arrives as ticks or candles.
2. Aggregation updates candle streams.
3. Strategies react in callbacks such as on_price_update(...) or on_candle_close(...).
4. Strategies request orders through the execution layer.
5. Portfolio and execution components apply gates, place or simulate orders, and emit lifecycle events.
6. Recording subscribers capture fills, positions, equity, and metrics.

Backtesting

Backtests use the same event model as live sessions.

• BacktestClient.from_dukascopy_cache(...) loads Dukascopy-backed history
• run_backtest(...) runs the event loop and recording pipeline
• Strategy, portfolio, and recording components behave the same way they do in live sessions

See docs/backtesting_guide.md for the current cache-backed workflow.

Live trading with IG

The IG integration provides REST-backed order execution, price streaming, and position synchronisation while keeping strategy code unchanged.

Live runs use these environment variables:

• IG_API_KEY
• IG_USERNAME
• IG_PASSWORD
• IG_ENVIRONMENT (DEMO by default, or LIVE)
• IG_ACCOUNT_ID for strategies that create tick-level MarketSubscription items

Example:

IG_API_KEY=... \
IG_USERNAME=... \
IG_PASSWORD=... \
IG_ENVIRONMENT=DEMO \
IG_ACCOUNT_ID=... \
python your_live_runner.py

tradedesk handles the short-lived IG session headers (CST and X-SECURITY-TOKEN) during authentication. They are not configured manually.

When live sessions request historical candles from IG, account-level historical-data limits are surfaced as a dedicated failure mode so embedding runtimes can back off or alert explicitly.

Machine learning support

tradedesk.ml is optional and installs behind the [ml] extra. It provides:

• Feature engineering over OHLC(V) and optional bid/ask data
• Label generation helpers
• Walk-forward cross-validation utilities
• Model wrappers and strategy integration points

See docs/ml_guide.md for the ML overview and docs/ml_labels_guide.md for label-specific details.

External data sources

tradedesk.data_sources exposes loaders and parsers for datasets that sit outside the live IG / Dukascopy execution paths.

Three free, no-auth macro feeds are supported:

• FRED — US rates (DFF, DGS3MO/2/10, T10Y2Y) and VIX from the St. Louis Fed
• ECB — EUR €STR, AAA government yield curve (3M–10Y) and Euribor from the ECB Data Portal
• CFTC COT — Commitment-of-Traders positioning for metals, energy, indices, 10Y notes and CME EUR/JPY/GBP currency futures, including the TFF dealer, asset-manager and leveraged-funds buckets

Series are materialized to a Parquet lake under the existing market-data root and loaded uniformly via load_macro_series / load_macro_frame:

from tradedesk.data_sources import load_macro_series, load_macro_frame

rates = load_macro_frame("FRED", ["DGS2", "DGS10", "VIXCLS"])
estr = load_macro_series("ECB", "EUR_ESTR")
eur_cot = load_macro_series("CFTC", "EURUSD")

A python -m tradedesk.data_sources.ingest CLI refreshes the lake on demand or on a weekly cron (idempotent; per-series failures are non-fatal).

See docs/data_sources_guide.md for the full series catalogue, the look-ahead semantics for CFTC release dates, and the lower-level CFTC COT API (CFTC_CONTRACTS, load_contract_history, download_cot_zip, iter_cot_rows, cot_release_date).

Documentation

Start with:

• docs/strategy_guide.md
• docs/backtesting_guide.md
• docs/portfolio_guide.md
• docs/risk_management.md
• docs/indicator_guide.md
• docs/aggregation_guide.md
• docs/metrics_guide.md
• docs/settings.md
• docs/operational_resilience.md
• docs/crash-recovery.md
• docs/data_sources_guide.md
• docs/ml_guide.md

Contributing

See CONTRIBUTING.md for development setup, quality gates, and PR expectations.

License

Licensed under the Apache License, Version 2.0. See: https://www.apache.org/licenses/LICENSE-2.0

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