3 Commits

Author	SHA1	Message	Date
Brent Neale	dce54845c2	Phase 1: Event-driven backtester, 5 strategies, and baseline results ... - Built event-driven backtesting engine with spread/slippage modeling, 3-TP partial closes, trailing stops, and rich trade logging (20+ features) - Implemented 5 strategy signal generators (MA Breakout, VWAP Reversal, Key Level Breakout, EMA Ribbon Scalp, Momentum Exhaustion) - Full indicator library (EMA, SMA, RSI, ATR, MACD, ADX, Stochastic, Session VWAP bands, swing points, key levels, RSI divergence) - Data pipeline: Dukascopy download, validation, 70/30 train/test split - Baseline results: all 5 strategies generate 200+ trades on training data (Jan 2021 - Aug 2023), best profit factors 0.82-0.96 on select pairs - Trade logs and reports saved for Phase 3 ML feature engineering Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-18 06:04:40 +10:00
Brent Neale	546d7311ec	Add economic calendar module to filter trades near high-impact events ... Introduces a full Trading Economics API pipeline that fetches, stores, and queries economic events (NFP, CPI, rate decisions, etc.) so the backtester can block trade entries within a configurable buffer window of high-impact releases — reducing slippage and false signals. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-17 20:00:28 +10:00
Brent Neale	da64544d3a	Add granularity tracking, drop intermediate columns, add SQL schema ... Track granularity (M1/M5) through the pipeline so rows are distinguishable after upload. Drop helper columns (tr, typical_price, pv) from the feature DataFrame. Add SQL schema with composite PK on (time, instrument, granularity). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-16 15:34:13 +10:00