S8_OB PASS: best params DISP_ATR=2.5, TP1=2.0, Window=40
(IS PF=1.39, OOS PF=1.59, Gen=1.382). S9_Filtered also PASS.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implemented two M15 intraday strategies to diversify the portfolio:
- S10: VWAP mean reversion in ranging markets (ADX<30, RSI(9) extremes)
- S11: ADX trend pullback to 20 EMA in strong trends (ADX>30, rising)
Added rsi_9 and atr_10 to the indicator pipeline for both strategies.
Backtested on IS (2021-2022) and OOS (2023): both strategies produced
insufficient trade counts on M15 and failed generalization. S10 best
result was EUR_GBP at Gen 0.65 (WARN). S11 collapsed to 0% WR OOS
across all param sweep combos. Both dropped from active portfolio —
3-strategy core (S7_Tight, S9_Filtered, S3) remains unchanged.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
S7: add RSI floor (RSI<40 = 0% WR) and ATR percentile cap (high-vol
regime = worse RR). IS flips from PF 0.68 to 1.52, OOS holds at 1.80.
S3: add confluence gate (C>=4) and skip hours 09-10 (0% WR). IS PF
1.06 -> 1.22, OOS PF 1.07 -> 1.23.
S9_Filtered: add skip_monday (unreliable Asian ranges after weekend
gaps). IS PF 1.10 -> 1.31, OOS holds strong at 2.26.
Drop S9/GBP_USD (negative PF across all param combos) and S4F/EUR_AUD
(overfit: IS 1.43 collapses to OOS 0.48). 3-strategy portfolio: all
PASS generalization, IS PF 1.29, OOS PF 1.55, Gen 1.46.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Externalize hardcoded params in S4F (5 params) and S3 (9 params) as class
attributes for sweep compatibility. Add unified backtest runner with IS/OOS
validation and generalization scores, plus parameter grid sweep (90 combos)
with OOS validation. S7/S9/S9_Filtered pass generalization; S4F/S3 confirm
defaults are near-optimal.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Railway cloud deployment section with service URLs, env vars,
persistent volume, and known limitations. Update project structure
with railway.toml and health.py. Update dashboard and bot run sections.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Dockerfile: add templates copy, ENV PORT, update CMD to live engine
- Create src/live/health.py: threaded HTTP health server (/health, /state)
- Wire health server into src/live/run.py before engine loop
- Dashboard: add unauthenticated /health endpoint, use PORT env var
- Create railway.toml with Dockerfile builder and health check config
- docker-compose.yml: rename service to live-engine, add PORT env vars
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
