New strategy (pivot_retest_engulfing) that enters long/short trades at
pivot level retests confirmed by SMA 50 alignment and engulfing candle
patterns. Uses ATR-based stop loss with two take-profit levels — at TP1
half the position closes and SL moves to breakeven, at TP2 the rest closes.
- data_engine: add detect_engulfing() for bullish/bearish pattern detection
- backtester: add generate_signals_pivot_retest(), run_backtest_dual_tp(),
update signal dispatcher and metrics for dual-TP trade format
- order_executor: support signal=-1 (SHORT), attach SL/TP levels
- config: switch to pivot_retest_engulfing with default params
- chart_trades: new mplfinance script to visualize entries on candlesticks
- README: rewrite with full setup guide, project structure, strategy docs
- requirements.txt: make portable (remove conda file:// paths), add mplfinance
- .env.example: add template for secrets
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Classic Pivot Points (P, R1-R3, S1-S3) computed from previous day's
OHLC data, displayed as horizontal lines on an interactive Plotly.js
candlestick chart at /chart with instrument/granularity selectors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Added parameter sweep tool that tested 320 combinations across SMA periods,
trade sizes, and RSI filters. Best result: SMA 50/100 on M15 with RSI 80/20
(Sharpe 5.69, 49% win rate). Updated backtester with RSI overbought/oversold
signal filtering and config to match optimal parameters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add Flask dashboard with status, config editor, logs, kill switch,
and backtest results pages with monthly P&L breakdowns
- Add historical_loader.py for paginated OANDA candle fetching (1yr+)
- Update backtester to $100k starting equity, monthly P&L computation,
and JSON summary output for dashboard display
- Update config to EUR_USD only on M5/M15 with SMA 21/50 strategy
- Add backtest.html template with performance metrics and bar charts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Dockerize the order executor with python:3.11-slim, add docker-compose
with config volume mount for hot-reload of system.yaml settings. Integrate
AI ensemble validation into order execution pipeline and add configurable
interval loop (default 60s) to replace container restart cycling.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Reads features from Supabase, simulates SMA(3)/SMA(20) crossover signals,
and outputs P&L, drawdown, and trade logs per instrument/granularity.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sanitize NaN/inf values that pandas re-introduces into float columns
after conversion, preventing JSON serialization errors. Fix upsert
response check to detect success via resp.data instead of status_code.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
- Create config/system.yaml with full master prompt template
- Create src/config_loader.py to load YAML config and .env
- Update data_engine.build_all_features() to support lists of windows
(e.g. sma_windows: [3, 20] produces sma_3 and sma_20 columns)
- Rewrite get_candles.py to loop over all instruments × granularities
from config instead of hardcoding EUR_USD/M5
- Fix supabase_upload.py indentation bug and wire up config for table name
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Brent Neale