Strip backtesting, rebalance indicator weights toward leading signals
Remove backtest.py, backtest_gpu.py, metrics.py, local CSV data, Dukascopy loader, and all backtest CLI modes from main.py. Rebalance engine weights: leading indicators (divergence, RSI, stochastic, BB) now carry 66% of signal weight vs 34% for lagging (EMA, MACD). Add ws_twelvedata.py for live tick streaming. Drop pyarrow and tqdm deps.
This commit is contained in:
Harwinder
-450
@@ -1,450 +0,0 @@
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# ForexBot2 — Engine Logic & Design Reference
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> USD/JPY signal intelligence platform.
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> Last updated: 2026-05-08
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---
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## Table of Contents
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1. [Architecture Overview](#1-architecture-overview)
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2. [Data Pipeline](#2-data-pipeline)
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3. [Indicator Engine (`engine.py`)](#3-indicator-engine)
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4. [Signal Aggregator (`signal_aggregator.py`)](#4-signal-aggregator)
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5. [Composite Scoring Formula](#5-composite-scoring-formula)
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6. [Indicator Weights — Optimised](#6-indicator-weights--optimised)
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7. [Market Regime Classification](#7-market-regime-classification)
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8. [EMA200 Distance Gate](#8-ema200-distance-gate)
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9. [Multi-Timeframe Alignment](#9-multi-timeframe-alignment)
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10. [Signal Grade System](#10-signal-grade-system)
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11. [GPU Backtester (`backtest_gpu.py`)](#11-gpu-backtester)
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12. [Backtesting Results Summary](#12-backtesting-results-summary)
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13. [Filter Catalogue](#13-filter-catalogue)
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14. [Configuration Reference](#14-configuration-reference)
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---
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## 1. Architecture Overview
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```
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market_data/ (local CSV)
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│
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▼
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ForexDataProvider ← also pulls live via Twelve Data SDK
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│
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▼
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TechnicalEngine ← computes all indicators bar-by-bar
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│
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▼
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SignalAggregator ← MTF blending, EMA200 gate, grading
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│
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├──▶ Live output (main.py)
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└──▶ GPUBacktester ← vectorised O(N) replay over full history
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```
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**Key design principle:** No single indicator is trusted alone. The composite score is a confidence-weighted, regime-adjusted blend of up to 7 indicators across 3 timeframes. A signal only fires when direction, strength, and confidence all agree.
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---
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## 2. Data Pipeline
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### Live mode
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`ForexDataProvider.fetch_ohlcv()` calls the Twelve Data SDK with in-memory caching (5-min TTL). Fetches primary TF + two confirmation TFs + optional regime TF.
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### Backtest mode
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`ForexDataProvider.load_local_csv()` reads MetaTrader tab-delimited exports from `market_data/`:
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| File | TF | Rows | Date Range |
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|------|----|------|------------|
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| USDJPY_M15.csv | M15 | 100,000 | 2022-05-04 → 2026-05-08 |
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| USDJPY_H1.csv | H1 | 100,000 | 2010-04-23 → 2026-05-08 |
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| USDJPY_H4.csv | H4 | 25,855 | 2010-04-22 → 2026-05-08 |
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| USDJPY_D1.csv | D1 | 5,020 | 2010-04-22 → 2026-05-08 |
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CSV format: tab-delimited, columns `Time | Open | High | Low | Close | Volume | Spread`. Spread column is dropped on load. Timestamps are UTC.
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**Data validation** (applied to both sources):
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- Drop NaN OHLC rows; forward-fill up to 3 bars
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- Drop future timestamps (>5 min ahead of UTC now)
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- Fix OHLC inconsistencies (High < Low etc.)
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- Remove outlier bars where `|return| > 10 × rolling_std`
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---
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## 3. Indicator Engine
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`engine.py → TechnicalEngine.analyze(df, symbol, timeframe)`
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All indicators are computed on a single OHLCV DataFrame. Each returns an `IndicatorSignal` with:
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- `value` — raw numeric reading
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- `signal` — `SignalStrength` enum: STRONG_BUY(+2) / BUY(+1) / NEUTRAL(0) / SELL(-1) / STRONG_SELL(-2)
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- `confidence` — 0–100%
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- `weight` — contribution to composite score (0 = excluded)
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### Indicators computed
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| Name | Key | Wilder smoothing? | Notes |
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|------|-----|:-----------------:|-------|
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| EMA Alignment | `ema_trend` | No | 4-EMA stack: 9/21/50/200; normalised to -1..+1 |
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| RSI | `rsi` | Yes (`alpha=1/14`) | Overbought/oversold + momentum zones |
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| MACD | `macd` | No | 12/26/9; crossover = STRONG signal |
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| Bollinger Bands | `bollinger` | No | 20-period; position within band + squeeze detect |
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| Stochastic | `stochastic` | No | 14/3; K+D both OB/OS required for signal |
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| ADX | `adx` | Yes | DI+/DI- direction + ADX strength gate (≥25) |
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| ATR | `atr` | Yes | Context only — sets SL/TP distance; weight=0 |
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| Ichimoku | `ichimoku` | No | Price vs cloud (+2/-2) + TK cross (+1/-1) + cloud colour (+1/-1) |
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| Divergence | `divergence` | — | RSI/MACD price vs indicator swing divergence; weight=1.5 |
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| Candlestick | `candlestick` | — | **Disabled** (ablation: net -3.4%) |
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| Pivot Points | `pivot` | — | **Disabled** (ablation: net -9.5%) |
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| Volume | `volume` | — | Context filter; weight=0 |
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**Disabled indicators** are controlled by `TechnicalConfig.disabled_indicators` (default: `["pivot", "candlestick"]`).
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---
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## 4. Signal Aggregator
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`signal_aggregator.py → SignalAggregator.analyze()`
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Orchestrates three timeframes (primary + 2 confirmation), blends their scores, applies gates, and emits a final graded signal.
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### Flow
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```
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H1 (primary) ──┐
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M15 (confirm) ──┼──▶ per-TF score ──▶ MTF blend ──▶ EMA200 gate ──▶ grade ──▶ output
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H4 (confirm) ──┘
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```
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### MTF alignment (no lookahead)
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Lower-frequency bars are aligned to the primary TF index by forward-filling at **close times**:
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```
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secondary close time = secondary bar open + secondary TF minutes
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primary close time = primary bar open + primary TF minutes
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For each primary bar T: use the most recent secondary close ≤ T_close
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```
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This ensures no future information leaks into the signal.
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---
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## 5. Composite Scoring Formula
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Mirrors `engine._compute_composite` exactly:
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```
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composite_score = clip(
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(Σ signal_i × weight_i × regime_mult_i × (confidence_i / 100))
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────────────────────────────────────────────────────────────────── × 50,
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(Σ weight_i × regime_mult_i)
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-100, +100
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)
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avg_confidence = Σ(confidence_i × weight_i) / Σ weight_i
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```
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- `signal_i` ∈ {-2, -1, 0, +1, +2}
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- `weight_i` — per-indicator base weight (see §6)
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- `regime_mult_i` — boosted/dampened by market regime (see §7)
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- Indicators with `weight == 0` are excluded entirely
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---
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## 6. Indicator Weights — Optimised
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Optimised 2026-05-08 via 36-combo sweep (ablation + ×0.5/1.5/2.0/3.0 per indicator) on H4 data 2022–2026. Combined result: **+21.30% return / -8.52% max DD / Sharpe 0.47** vs baseline +7.12% / -9.05% DD / Sharpe 0.23.
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| Indicator | Weight | Change from original | Reason |
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|-----------|-------:|---------------------|--------|
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| `ema_trend` | **3.6** | was 1.2 (×3) | Strongest directional signal on trending pair |
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| `macd` | **3.3** | was 1.1 (×3) | Most impactful single indicator; ablation = worst result |
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| `stochastic` | **1.6** | was 0.8 (×2) | Clean timing signal; 40% WR at ×2 |
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| `adx` | **0.5** | was 1.0 (÷2) | Partially redundant with EMA stack |
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| `ichimoku` | **0.55** | was 1.1 (÷2) | Lags too much at H4; hurts on ranging periods |
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| `rsi` | **0.0** | was 0.3 (ablated) | Net drag across all test windows |
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| `bollinger` | **0.0** | was 0.9 (ablated) | Counter-trend noise on trending pair |
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| `divergence` | 1.5 | unchanged | Strong reversal signal; not in sweep scope |
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| `atr` | 0.0 | unchanged | Context only; sets SL/TP |
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> **Note:** RSI and Bollinger are still **computed and displayed** in live output for reference, but their weight=0 means they contribute nothing to the composite score.
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### Regime weight multipliers
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Applied on top of base weights:
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| Indicator | Trending mult | Ranging mult |
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|-----------|:-------------:|:------------:|
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| ema_trend | ×1.5 | ×0.6 |
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| adx | ×1.3 | ×0.5 |
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| macd | ×1.2 | ×1.0 |
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| ichimoku | ×1.3 | ×0.2 |
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| rsi | ×0.7 | ×1.4 |
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| bollinger | ×0.6 | ×1.4 |
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| stochastic | ×1.0 | ×1.3 |
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In trending regimes: EMA trend, MACD, and Ichimoku are boosted further.
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In ranging regimes: RSI and Bollinger would be boosted — but since both are weight=0, ranging regime currently relies entirely on stochastic + reduced EMA/ADX.
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---
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## 7. Market Regime Classification
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`engine._classify_regime()` — runs after all indicators are computed:
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```
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if ADX ≥ 25:
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if EMA signal is BUY or STRONG_BUY → trending_up
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if EMA signal is SELL or STRONG_SELL → trending_down
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else → trending
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else:
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→ ranging
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```
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Regime feeds directly into the weight multipliers applied in `_compute_composite`.
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---
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## 8. EMA200 Distance Gate
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**Strongest single filter discovered** — implemented in `signal_aggregator.py`.
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```
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ema_dist = |close - EMA200| / EMA200 × 100 (percent)
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if ema_dist < 0.5% or ema_dist > 1.5%:
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confidence *= 0.55 ← damped
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else: ← sweet spot
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no change
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```
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**Rationale:** Price within 0.5% of EMA200 is in a high-chop zone where the 200 EMA acts as contested support/resistance. Price more than 1.5% away has often already run far and is prone to reversion. The 0.5–1.5% band is where trending momentum is cleanest.
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**Impact measured:** Lifted cumulative return +13.88% → +35.50% in the 2024-trend ablation window.
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---
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## 9. Multi-Timeframe Alignment
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Primary TF: **H1** (default). Confirmation TFs: **M15** and **H4**.
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### MTF blending (when 2 of 3 TFs agree)
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```
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mtf_agree = (bull_count ≥ 2) OR (bear_count ≥ 2)
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if mtf_agree:
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score = 0.6 × primary_score + 0.4 × mean(all_scores)
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confidence = min(confidence + 15, 95)
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else:
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confidence = confidence × 0.7 ← penalised for disagreement
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```
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### MTF agreement bonus in grade assignment
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`mtf_agree=True` is required for grades A and A+.
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---
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## 10. Signal Grade System
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| Grade | Conditions |
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|-------|-----------|
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| **A+** | `|score| ≥ 50` AND `conf ≥ 75%` AND `mtf_agree` |
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| **A** | `|score| ≥ 35` AND `conf ≥ 65%` AND `mtf_agree` |
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| **B** | `|score| ≥ 25` AND `conf ≥ 55%` |
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| **C** | `|score| ≥ 15` AND `conf ≥ 45%` |
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| **D** | `|score| ≥ 10` |
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| **F** | below all thresholds |
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Default minimum grade for trade entry: **B** (`BacktestConfig.min_trade_grade`).
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### Signal integer thresholds (for trade direction)
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| Signal | Condition |
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|--------|-----------|
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| STRONG_BUY (+2) | score ≥ +40 AND conf ≥ 65% |
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| BUY (+1) | score ≥ +15 AND conf ≥ 50% |
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| STRONG_SELL (-2) | score ≤ -40 AND conf ≥ 65% |
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| SELL (-1) | score ≤ -15 AND conf ≥ 50% |
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| NEUTRAL | otherwise |
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---
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## 11. GPU Backtester
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`backtest_gpu.py` — completely separate from `backtest.py`.
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### Why O(N) instead of O(N²)
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`backtest.py` slices the DataFrame and recomputes all indicators fresh each bar → N × O(N) = **O(N²)**.
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`backtest_gpu.py` computes every indicator series end-to-end once, then reads bar T's value in O(1) → **O(N) total**.
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On 100,000 H1 bars: ~4 seconds vs >10 minutes.
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### Two-phase architecture
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```
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Phase 1 — prepare_arrays(dfs)
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VectorIndicators.compute_all() ← pandas ewm/rolling, C-optimised
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Align MTF signals to primary TF ← forward-fill at close times
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Compute regime ADX series ← if regime filter enabled
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Returns reusable bundle dict
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Phase 2 — score_and_run(bundle, weights, disabled)
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compute_bar_scores() ← CuPy GPU or NumPy CPU
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compute_final_scores() ← MTF blend + EMA200 gate (vectorised)
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compute_grades() ← vectorised grade assignment
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run_state_machine() ← sequential position management (O(N))
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```
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`prepare_arrays()` is called once; `score_and_run()` can be called many times with different weights — this is what the `--optimize` sweep uses.
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### Optimization sweep (`--optimize`)
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36 combinations tested per run:
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- **1** baseline (default weights)
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- **7** ablation runs (each indicator disabled one at a time)
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- **28** weight multiplier runs (each indicator at ×0.5, ×1.5, ×2.0, ×3.0)
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Indicator arrays computed once → all 36 replays run in seconds.
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### Trade execution model
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```
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Entry: next bar Open ± (spread/2 + slippage) [default: 0.5 pip slippage]
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SL: entry ± ATR × atr_sl_multiplier [default: 1.5×]
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TP: entry ± SL_dist × rr_ratio [default: 2.0× = 1:2 R:R]
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Risk: 1% of current equity per trade
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```
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If SL and TP both touch within the same bar: SL assumed to hit first (`sl_first_on_tie=True`).
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### CLI flags
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```
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python backtest_gpu.py --tf H4 --local # local CSV, H4
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python backtest_gpu.py --tf H1 --local --start 2022-01-01
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python backtest_gpu.py --tf H4 --local --optimize # 36-combo weight sweep
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python backtest_gpu.py --regime-filter --regime-tf D1
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python backtest_gpu.py --session-filter # UTC 07-16 only
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python backtest_gpu.py --journal --journal-csv out.csv
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```
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---
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## 12. Backtesting Results Summary
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### H4 — local CSV 2022–2026 (optimised weights)
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| Metric | Value |
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|--------|-------|
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| Trades | 160 |
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| Win Rate | 38.1% |
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| Profit Factor | 1.19 |
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| Total Return | +21.30% |
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| Max Drawdown | -8.52% |
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| Sharpe | 0.47 |
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|
||||||
| Sortino | 1.91 |
|
|
||||||
|
|
||||||
### D1 — Twelve Data 2007–2026 (optimised weights)
|
|
||||||
|
|
||||||
| Metric | Value |
|
|
||||||
|--------|-------|
|
|
||||||
| Trades | 62 |
|
|
||||||
| Win Rate | 40.3% |
|
|
||||||
| Profit Factor | 1.27 |
|
|
||||||
| Total Return | +10.51% |
|
|
||||||
| Max Drawdown | -6.84% |
|
|
||||||
| Sortino | 7.74 |
|
|
||||||
|
|
||||||
### Optimisation sweep delta (H4 2022–2026)
|
|
||||||
|
|
||||||
| | Baseline | Optimised |
|
|
||||||
|--|----------|-----------|
|
|
||||||
| Return | +7.12% | +21.30% |
|
|
||||||
| Max DD | -9.05% | -8.52% |
|
|
||||||
| Sharpe | 0.23 | 0.47 |
|
|
||||||
| Win Rate | 36.3% | 38.1% |
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 13. Filter Catalogue
|
|
||||||
|
|
||||||
### Regime filter (`--regime-filter`)
|
|
||||||
|
|
||||||
Gates trade entry on ADX of a **higher** timeframe than the primary. Default: H4 ADX gate when trading H1. Redundant if regime TF == primary TF.
|
|
||||||
|
|
||||||
```
|
|
||||||
regime TF ADX ≥ min_adx (default 25) → entry allowed
|
|
||||||
```
|
|
||||||
|
|
||||||
Sensible defaults: H1→H4, H4→D1, D1→D1.
|
|
||||||
|
|
||||||
### Session filter (`--session-filter`)
|
|
||||||
|
|
||||||
Only trade during UTC 07:00–16:00 (London + NY overlap).
|
|
||||||
2026 diagnostic: 07-15 UTC = 47% WR / +127 pips; off-hours = 0% WR / -571 pips.
|
|
||||||
|
|
||||||
### Auto-session toggle (`auto_session_filter`)
|
|
||||||
|
|
||||||
Reactive version of session filter. Activates automatically when mean P&L over last N closed trades drops below threshold:
|
|
||||||
|
|
||||||
```
|
|
||||||
if mean(pnl_pct[-10:]) < -0.3%:
|
|
||||||
session_filter = ON ← restrict to liquid hours
|
|
||||||
else:
|
|
||||||
session_filter = OFF ← all hours allowed
|
|
||||||
```
|
|
||||||
|
|
||||||
### EMA200 distance gate
|
|
||||||
|
|
||||||
Always active. See §8.
|
|
||||||
|
|
||||||
### Signal grade gate (`min_trade_grade`)
|
|
||||||
|
|
||||||
Default: **B**. Only trades with grade ≥ B are entered. Raise to A for fewer, higher-quality trades.
|
|
||||||
|
|
||||||
### Min absolute score (`min_abs_score`)
|
|
||||||
|
|
||||||
Default: **0** (no extra gate). Set to e.g. 15 to skip weak directional scores.
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 14. Configuration Reference
|
|
||||||
|
|
||||||
All knobs live in `config.py → PlatformConfig`.
|
|
||||||
|
|
||||||
```python
|
|
||||||
# Indicator parameters
|
|
||||||
TechnicalConfig.ema_fast = 9
|
|
||||||
TechnicalConfig.ema_medium = 21
|
|
||||||
TechnicalConfig.ema_slow = 50
|
|
||||||
TechnicalConfig.ema_trend = 200
|
|
||||||
TechnicalConfig.rsi_period = 14
|
|
||||||
TechnicalConfig.macd_fast / slow / signal = 12/26/9
|
|
||||||
TechnicalConfig.bb_period = 20
|
|
||||||
TechnicalConfig.atr_period = 14
|
|
||||||
TechnicalConfig.stoch_k / d = 14/3
|
|
||||||
TechnicalConfig.adx_period = 14
|
|
||||||
TechnicalConfig.adx_strong_trend = 25.0
|
|
||||||
TechnicalConfig.ichimoku_tenkan / kijun / senkou_b = 9/26/52
|
|
||||||
TechnicalConfig.disabled_indicators = ["pivot", "candlestick"]
|
|
||||||
|
|
||||||
# Signal scoring
|
|
||||||
SignalConfig.min_confidence = 55.0
|
|
||||||
SignalConfig.atr_sl_multiplier = 1.5
|
|
||||||
SignalConfig.default_rr_ratio = 2.0
|
|
||||||
SignalConfig.default_risk_pct = 1.0
|
|
||||||
|
|
||||||
# Backtest
|
|
||||||
BacktestConfig.initial_balance = 10_000
|
|
||||||
BacktestConfig.risk_per_trade_pct = 1.0
|
|
||||||
BacktestConfig.min_trade_grade = "B"
|
|
||||||
BacktestConfig.regime_filter = False
|
|
||||||
BacktestConfig.session_filter = False
|
|
||||||
BacktestConfig.auto_session_filter = False
|
|
||||||
BacktestConfig.cache_dir = ".cache"
|
|
||||||
```
|
|
||||||
@@ -1,42 +0,0 @@
|
|||||||
# Running ForexBot2 on Linux
|
|
||||||
|
|
||||||
## Install dependencies
|
|
||||||
|
|
||||||
```bash
|
|
||||||
python -m venv .venv
|
|
||||||
source .venv/bin/activate
|
|
||||||
pip install -r requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
## Telegram bot
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# Start (runs in background, logs to file)
|
|
||||||
nohup .venv/bin/python telegram_bot.py >> bot.log 2>&1 &
|
|
||||||
|
|
||||||
# Follow logs
|
|
||||||
tail -f bot.log
|
|
||||||
|
|
||||||
# Check it is running
|
|
||||||
pgrep -a -f telegram_bot.py
|
|
||||||
|
|
||||||
# Stop
|
|
||||||
pkill -f telegram_bot.py
|
|
||||||
```
|
|
||||||
|
|
||||||
## Live signal (CLI)
|
|
||||||
|
|
||||||
```bash
|
|
||||||
.venv/bin/python main.py # H1 summary
|
|
||||||
.venv/bin/python main.py --detail # full indicator breakdown
|
|
||||||
.venv/bin/python main.py --json # JSON output
|
|
||||||
```
|
|
||||||
|
|
||||||
## Backtest
|
|
||||||
|
|
||||||
```bash
|
|
||||||
.venv/bin/python main.py --backtest
|
|
||||||
.venv/bin/python main.py --backtest --optimize
|
|
||||||
```
|
|
||||||
|
|
||||||
> No GPU required — GPU code is only used by `backtest_gpu.py`.
|
|
||||||
-667
@@ -1,667 +0,0 @@
|
|||||||
"""
|
|
||||||
ForexBot2 — Walk-Forward Backtester + Parameter Optimizer
|
|
||||||
==========================================================
|
|
||||||
Replays Twelve Data USD/JPY history bar-by-bar through the same
|
|
||||||
SignalAggregator that drives live mode. The same code path that
|
|
||||||
trades USD/JPY in production is what produces these metrics — so
|
|
||||||
the backtest validates the engine, not a parallel implementation.
|
|
||||||
"""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import copy
|
|
||||||
import itertools
|
|
||||||
import logging
|
|
||||||
import multiprocessing as mp
|
|
||||||
import os
|
|
||||||
from collections import deque
|
|
||||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
|
||||||
from dataclasses import replace
|
|
||||||
from typing import Deque, Dict, List, Optional, Tuple
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
from config import PlatformConfig, Timeframe, USDJPY, PIP_VALUE
|
|
||||||
from data_provider import ForexDataProvider
|
|
||||||
from metrics import BacktestResult, Trade, compute_metrics, trades_to_dataframe
|
|
||||||
from signal_aggregator import SignalAggregator, SignalReport, TradeSetup
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
|
|
||||||
# Period in minutes for each timeframe (used for close-time computation)
|
|
||||||
TF_MINUTES: Dict[Timeframe, int] = {
|
|
||||||
Timeframe.M1: 1,
|
|
||||||
Timeframe.M5: 5,
|
|
||||||
Timeframe.M15: 15,
|
|
||||||
Timeframe.M30: 30,
|
|
||||||
Timeframe.H1: 60,
|
|
||||||
Timeframe.H4: 240,
|
|
||||||
Timeframe.D1: 1440,
|
|
||||||
Timeframe.W1: 10080,
|
|
||||||
}
|
|
||||||
|
|
||||||
GRADE_ORDER = ["A+", "A", "B", "C", "D", "F"]
|
|
||||||
|
|
||||||
|
|
||||||
def _grade_passes(grade: str, min_grade: str) -> bool:
|
|
||||||
"""True if `grade` is at least as good as `min_grade`."""
|
|
||||||
if grade not in GRADE_ORDER or min_grade not in GRADE_ORDER:
|
|
||||||
return False
|
|
||||||
return GRADE_ORDER.index(grade) <= GRADE_ORDER.index(min_grade)
|
|
||||||
|
|
||||||
|
|
||||||
# ══════════════════════════════════════════════════════════════════
|
|
||||||
# WALK-FORWARD BACKTESTER
|
|
||||||
# ══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
class WalkForwardBacktester:
|
|
||||||
"""Single-pair, single-position event-driven backtester."""
|
|
||||||
|
|
||||||
def __init__(self, config: PlatformConfig):
|
|
||||||
self.config = config
|
|
||||||
self.aggregator = SignalAggregator(config)
|
|
||||||
self.data_provider = self.aggregator.data_provider
|
|
||||||
self._regime_adx: Optional[pd.Series] = None
|
|
||||||
self._regime_tf: Optional[Timeframe] = None
|
|
||||||
|
|
||||||
# ── Public ─────────────────────────────────────────────────────
|
|
||||||
|
|
||||||
def load_data(self) -> Dict[Timeframe, pd.DataFrame]:
|
|
||||||
"""Pull (or read from disk cache) all needed timeframes."""
|
|
||||||
timeframes = [self.config.primary_timeframe] + list(
|
|
||||||
self.config.confirmation_timeframes
|
|
||||||
)
|
|
||||||
bt = self.config.backtest
|
|
||||||
if bt.regime_filter and bt.regime_filter_timeframe not in timeframes:
|
|
||||||
timeframes.append(bt.regime_filter_timeframe)
|
|
||||||
|
|
||||||
dfs: Dict[Timeframe, pd.DataFrame] = {}
|
|
||||||
for tf in timeframes:
|
|
||||||
df = self.data_provider.fetch_full_history(
|
|
||||||
symbol=self.config.symbol,
|
|
||||||
timeframe=tf,
|
|
||||||
max_bars=bt.max_bars,
|
|
||||||
start_date=bt.start_date,
|
|
||||||
end_date=bt.end_date,
|
|
||||||
)
|
|
||||||
df = self._apply_window(df)
|
|
||||||
dfs[tf] = df
|
|
||||||
return dfs
|
|
||||||
|
|
||||||
def _precompute_all_signals(
|
|
||||||
self,
|
|
||||||
dfs: Dict[Timeframe, pd.DataFrame],
|
|
||||||
primary_df: pd.DataFrame,
|
|
||||||
warmup: int,
|
|
||||||
progress: bool = True,
|
|
||||||
) -> Dict[int, "SignalReport"]:
|
|
||||||
"""Pre-compute signals for every bar in parallel using all CPU threads.
|
|
||||||
|
|
||||||
TechnicalEngine.analyze() is stateless (pure function of the df slice),
|
|
||||||
so concurrent calls are safe. NumPy/pandas C extensions release the GIL,
|
|
||||||
giving real parallelism across threads without pickling overhead.
|
|
||||||
Returns bar-index → SignalReport so the sequential state machine can
|
|
||||||
do O(1) lookups instead of recomputing indicators per bar.
|
|
||||||
"""
|
|
||||||
primary_tf = self.config.primary_timeframe
|
|
||||||
# Use all logical threads; cap at 2× physical cores to avoid over-subscription
|
|
||||||
n_workers = min(os.cpu_count() or 4, 16)
|
|
||||||
bar_indices = list(range(warmup, len(primary_df) - 1))
|
|
||||||
|
|
||||||
def _compute_bar(t: int) -> Tuple[int, "SignalReport"]:
|
|
||||||
decision_time = self._bar_close_time(primary_df.index[t], primary_tf)
|
|
||||||
sliced = self._slice_dfs(dfs, decision_time)
|
|
||||||
report = self.aggregator.analyze_from_dataframes(
|
|
||||||
symbol=self.config.symbol,
|
|
||||||
dfs=sliced,
|
|
||||||
use_news=False,
|
|
||||||
)
|
|
||||||
return t, report
|
|
||||||
|
|
||||||
precomputed: Dict[int, "SignalReport"] = {}
|
|
||||||
pbar = None
|
|
||||||
if progress:
|
|
||||||
try:
|
|
||||||
from tqdm import tqdm # type: ignore
|
|
||||||
pbar = tqdm(
|
|
||||||
total=len(bar_indices),
|
|
||||||
desc=f"Signals ({n_workers} threads)",
|
|
||||||
unit="bar",
|
|
||||||
leave=False,
|
|
||||||
)
|
|
||||||
except ImportError:
|
|
||||||
pass
|
|
||||||
|
|
||||||
with ThreadPoolExecutor(max_workers=n_workers) as executor:
|
|
||||||
futures = {executor.submit(_compute_bar, t): t for t in bar_indices}
|
|
||||||
for future in as_completed(futures):
|
|
||||||
t, report = future.result()
|
|
||||||
precomputed[t] = report
|
|
||||||
if pbar:
|
|
||||||
pbar.update(1)
|
|
||||||
|
|
||||||
if pbar:
|
|
||||||
pbar.close()
|
|
||||||
|
|
||||||
return precomputed
|
|
||||||
|
|
||||||
def run(
|
|
||||||
self,
|
|
||||||
dfs: Optional[Dict[Timeframe, pd.DataFrame]] = None,
|
|
||||||
progress: bool = True,
|
|
||||||
) -> BacktestResult:
|
|
||||||
if dfs is None:
|
|
||||||
dfs = self.load_data()
|
|
||||||
|
|
||||||
self._precompute_regime_filter(dfs)
|
|
||||||
|
|
||||||
primary_tf = self.config.primary_timeframe
|
|
||||||
primary_df = dfs.get(primary_tf)
|
|
||||||
if primary_df is None or primary_df.empty:
|
|
||||||
return BacktestResult(initial_balance=self.config.backtest.initial_balance)
|
|
||||||
|
|
||||||
# Warmup: enough bars for ema_trend & ichimoku_senkou_b on the primary TF
|
|
||||||
warmup = max(
|
|
||||||
self.config.technical.ema_trend,
|
|
||||||
self.config.technical.ichimoku_senkou_b,
|
|
||||||
200,
|
|
||||||
) + 30
|
|
||||||
|
|
||||||
if len(primary_df) <= warmup + 1:
|
|
||||||
logger.warning(
|
|
||||||
f"Insufficient data: {len(primary_df)} primary bars, "
|
|
||||||
f"need >{warmup}."
|
|
||||||
)
|
|
||||||
return BacktestResult(initial_balance=self.config.backtest.initial_balance)
|
|
||||||
|
|
||||||
# Pre-compute all bar signals in parallel before the sequential replay
|
|
||||||
precomputed = self._precompute_all_signals(
|
|
||||||
dfs, primary_df, warmup, progress=progress
|
|
||||||
)
|
|
||||||
|
|
||||||
bt = self.config.backtest
|
|
||||||
balance = bt.initial_balance
|
|
||||||
equity_points: List[Tuple[pd.Timestamp, float]] = [
|
|
||||||
(primary_df.index[warmup], balance)
|
|
||||||
]
|
|
||||||
trades: List[Trade] = []
|
|
||||||
|
|
||||||
# Auto-session feedback: rolling pnl_pct over last K closed trades.
|
|
||||||
# When mean falls below threshold, the session filter activates;
|
|
||||||
# once expectancy recovers, it releases.
|
|
||||||
self._recent_pnl_pcts: Deque[float] = deque(
|
|
||||||
maxlen=max(1, bt.auto_session_lookback_trades)
|
|
||||||
)
|
|
||||||
|
|
||||||
open_position: Optional[Dict] = None
|
|
||||||
|
|
||||||
# Optional progress bar for the state-machine replay pass
|
|
||||||
iterator = range(warmup, len(primary_df) - 1)
|
|
||||||
if progress:
|
|
||||||
try:
|
|
||||||
from tqdm import tqdm # type: ignore
|
|
||||||
iterator = tqdm(iterator, desc="Replaying bars", unit="bar", leave=False)
|
|
||||||
except ImportError:
|
|
||||||
pass
|
|
||||||
|
|
||||||
for t in iterator:
|
|
||||||
decision_time = self._bar_close_time(primary_df.index[t], primary_tf)
|
|
||||||
|
|
||||||
# ── Step 1: manage any open position ───────────────────
|
|
||||||
if open_position is not None:
|
|
||||||
next_bar = primary_df.iloc[t]
|
|
||||||
exit_info = self._check_exit(open_position, next_bar)
|
|
||||||
if exit_info is not None:
|
|
||||||
closed = self._close_trade(
|
|
||||||
open_position, next_bar.name, exit_info, balance
|
|
||||||
)
|
|
||||||
trades.append(closed)
|
|
||||||
balance += closed.pnl_currency
|
|
||||||
equity_points.append((next_bar.name, balance))
|
|
||||||
self._recent_pnl_pcts.append(float(closed.pnl_pct))
|
|
||||||
open_position = None
|
|
||||||
|
|
||||||
# ── Step 2: maybe open a new position ──────────────────
|
|
||||||
if open_position is None or not bt.one_position_only:
|
|
||||||
report = precomputed[t]
|
|
||||||
|
|
||||||
if (
|
|
||||||
_grade_passes(report.signal_quality, bt.min_trade_grade)
|
|
||||||
and abs(float(report.final_score)) >= bt.min_abs_score
|
|
||||||
and report.trade_setup is not None
|
|
||||||
and t + 1 < len(primary_df)
|
|
||||||
and self._regime_passes(decision_time)
|
|
||||||
and self._session_passes(decision_time)
|
|
||||||
):
|
|
||||||
next_bar = primary_df.iloc[t + 1]
|
|
||||||
open_position = self._open_trade(report, next_bar, balance)
|
|
||||||
|
|
||||||
# ── Force-close any still-open position at end of data ────
|
|
||||||
if open_position is not None:
|
|
||||||
last_bar = primary_df.iloc[-1]
|
|
||||||
closed = self._close_trade(
|
|
||||||
open_position,
|
|
||||||
last_bar.name,
|
|
||||||
{"price": float(last_bar["Close"]), "reason": "end_of_data"},
|
|
||||||
balance,
|
|
||||||
)
|
|
||||||
trades.append(closed)
|
|
||||||
balance += closed.pnl_currency
|
|
||||||
equity_points.append((last_bar.name, balance))
|
|
||||||
|
|
||||||
# Build equity curve
|
|
||||||
eq_index = [p[0] for p in equity_points]
|
|
||||||
eq_values = [p[1] for p in equity_points]
|
|
||||||
equity_curve = pd.Series(eq_values, index=pd.DatetimeIndex(eq_index))
|
|
||||||
|
|
||||||
elapsed_days: Optional[float] = None
|
|
||||||
if len(equity_curve) >= 2:
|
|
||||||
elapsed_days = (equity_curve.index[-1] - equity_curve.index[0]).total_seconds() / 86400
|
|
||||||
|
|
||||||
metrics = compute_metrics(
|
|
||||||
trades, equity_curve, bt.initial_balance, elapsed_days=elapsed_days
|
|
||||||
)
|
|
||||||
|
|
||||||
return BacktestResult(
|
|
||||||
trades=trades,
|
|
||||||
equity_curve=equity_curve,
|
|
||||||
initial_balance=bt.initial_balance,
|
|
||||||
final_balance=balance,
|
|
||||||
metrics=metrics,
|
|
||||||
)
|
|
||||||
|
|
||||||
# ── Private: regime gate ───────────────────────────────────────
|
|
||||||
|
|
||||||
def _precompute_regime_filter(self, dfs: Dict[Timeframe, pd.DataFrame]) -> None:
|
|
||||||
"""Pre-compute ADX on the regime-filter TF; cached for the whole run."""
|
|
||||||
bt = self.config.backtest
|
|
||||||
if not bt.regime_filter:
|
|
||||||
self._regime_adx = None
|
|
||||||
self._regime_tf = None
|
|
||||||
return
|
|
||||||
|
|
||||||
tf = bt.regime_filter_timeframe
|
|
||||||
df = dfs.get(tf)
|
|
||||||
if df is None or df.empty:
|
|
||||||
logger.warning(
|
|
||||||
f"Regime filter requested ({tf.value}) but no data available; "
|
|
||||||
f"all signals will be skipped."
|
|
||||||
)
|
|
||||||
self._regime_adx = pd.Series(dtype=float)
|
|
||||||
self._regime_tf = tf
|
|
||||||
return
|
|
||||||
|
|
||||||
self._regime_tf = tf
|
|
||||||
self._regime_adx = self._compute_adx_series(df, self.config.technical.adx_period)
|
|
||||||
|
|
||||||
def _regime_passes(self, decision_time: pd.Timestamp) -> bool:
|
|
||||||
bt = self.config.backtest
|
|
||||||
if not bt.regime_filter:
|
|
||||||
return True
|
|
||||||
if self._regime_adx is None or self._regime_adx.empty:
|
|
||||||
return False
|
|
||||||
period_min = TF_MINUTES[self._regime_tf]
|
|
||||||
close_times = self._regime_adx.index + pd.Timedelta(minutes=period_min)
|
|
||||||
valid = self._regime_adx[close_times <= decision_time]
|
|
||||||
if valid.empty:
|
|
||||||
return False
|
|
||||||
last = valid.iloc[-1]
|
|
||||||
if pd.isna(last):
|
|
||||||
return False
|
|
||||||
return float(last) >= bt.regime_filter_min_adx
|
|
||||||
|
|
||||||
def _session_passes(self, decision_time: pd.Timestamp) -> bool:
|
|
||||||
"""UTC hour gate — decision_time = entry bar's open time.
|
|
||||||
|
|
||||||
Filter is active when EITHER manual `session_filter` is on, OR
|
|
||||||
`auto_session_filter` is on and the rolling mean pnl_pct over the
|
|
||||||
last N closed trades is below threshold.
|
|
||||||
"""
|
|
||||||
bt = self.config.backtest
|
|
||||||
active = bt.session_filter
|
|
||||||
if not active and bt.auto_session_filter:
|
|
||||||
recent = getattr(self, "_recent_pnl_pcts", None)
|
|
||||||
if recent is not None and len(recent) >= bt.auto_session_lookback_trades:
|
|
||||||
mean_pnl = sum(recent) / len(recent)
|
|
||||||
active = mean_pnl < bt.auto_session_threshold_pct
|
|
||||||
if not active:
|
|
||||||
return True
|
|
||||||
h = int(decision_time.hour)
|
|
||||||
return bt.session_start_hour <= h < bt.session_end_hour
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _compute_adx_series(df: pd.DataFrame, period: int = 14) -> pd.Series:
|
|
||||||
"""Wilder's ADX series — same algorithm as engine._compute_adx."""
|
|
||||||
import numpy as np
|
|
||||||
high, low, close = df["High"], df["Low"], df["Close"]
|
|
||||||
tr = pd.concat([
|
|
||||||
high - low,
|
|
||||||
(high - close.shift()).abs(),
|
|
||||||
(low - close.shift()).abs(),
|
|
||||||
], axis=1).max(axis=1)
|
|
||||||
plus_dm = high.diff()
|
|
||||||
minus_dm = -low.diff()
|
|
||||||
plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
|
|
||||||
minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
|
|
||||||
atr_s = tr.ewm(alpha=1/period, min_periods=period).mean()
|
|
||||||
plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
|
|
||||||
minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
|
|
||||||
dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
|
|
||||||
return dx.ewm(alpha=1/period, min_periods=period).mean()
|
|
||||||
|
|
||||||
# ── Private: data slicing ──────────────────────────────────────
|
|
||||||
|
|
||||||
def _apply_window(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
||||||
bt = self.config.backtest
|
|
||||||
if bt.start_date:
|
|
||||||
start = pd.Timestamp(bt.start_date, tz="UTC")
|
|
||||||
df = df[df.index >= start]
|
|
||||||
if bt.end_date:
|
|
||||||
end = pd.Timestamp(bt.end_date, tz="UTC")
|
|
||||||
df = df[df.index <= end]
|
|
||||||
return df
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _bar_close_time(open_time: pd.Timestamp, tf: Timeframe) -> pd.Timestamp:
|
|
||||||
return open_time + pd.Timedelta(minutes=TF_MINUTES[tf])
|
|
||||||
|
|
||||||
def _slice_dfs(
|
|
||||||
self, dfs: Dict[Timeframe, pd.DataFrame], decision_time: pd.Timestamp
|
|
||||||
) -> Dict[Timeframe, pd.DataFrame]:
|
|
||||||
"""Return only bars whose close time ≤ decision_time (no lookahead)."""
|
|
||||||
sliced: Dict[Timeframe, pd.DataFrame] = {}
|
|
||||||
for tf, df in dfs.items():
|
|
||||||
close_times = df.index + pd.Timedelta(minutes=TF_MINUTES[tf])
|
|
||||||
sliced[tf] = df[close_times <= decision_time]
|
|
||||||
return sliced
|
|
||||||
|
|
||||||
# ── Private: trade execution ───────────────────────────────────
|
|
||||||
|
|
||||||
def _open_trade(
|
|
||||||
self, report: SignalReport, next_bar: pd.Series, balance: float
|
|
||||||
) -> Dict:
|
|
||||||
"""Open at next bar's open, applying spread + slippage."""
|
|
||||||
ts: TradeSetup = report.trade_setup # type: ignore[assignment]
|
|
||||||
bt = self.config.backtest
|
|
||||||
|
|
||||||
raw_entry = float(next_bar["Open"])
|
|
||||||
|
|
||||||
# Sanity-test inversion: flip direction and mirror SL/TP around entry
|
|
||||||
direction = ts.direction
|
|
||||||
sl = ts.stop_loss
|
|
||||||
tp = ts.take_profit_1
|
|
||||||
if bt.invert_signal:
|
|
||||||
direction = "SELL" if direction == "BUY" else "BUY"
|
|
||||||
sl, tp = (2 * raw_entry - sl), (2 * raw_entry - tp)
|
|
||||||
|
|
||||||
# Cost in price units = (half-spread + slippage) × pip_value
|
|
||||||
half_spread_pips = 0.5 if bt.use_spread_cost else 0.0
|
|
||||||
cost_pips = half_spread_pips + bt.slippage_pips
|
|
||||||
cost_price = cost_pips * PIP_VALUE
|
|
||||||
|
|
||||||
if direction == "BUY":
|
|
||||||
entry_price = raw_entry + cost_price # buy higher (worse)
|
|
||||||
else:
|
|
||||||
entry_price = raw_entry - cost_price # sell lower (worse)
|
|
||||||
|
|
||||||
# Position sizing — risk a fixed % of current balance
|
|
||||||
risk_pct = bt.risk_per_trade_pct / 100.0
|
|
||||||
risk_amount = balance * risk_pct
|
|
||||||
risk_pips = max(ts.risk_pips, 1e-9)
|
|
||||||
pip_currency_value = risk_amount / risk_pips # $ per pip
|
|
||||||
|
|
||||||
# ── Diagnostic snapshot at entry ─────────────────────────
|
|
||||||
analysis = report.primary_analysis
|
|
||||||
adx_val = 0.0
|
|
||||||
atr_pct = 0.0
|
|
||||||
rsi_val = 50.0
|
|
||||||
ema_dist = 0.0
|
|
||||||
if analysis is not None:
|
|
||||||
adx_sig = analysis.indicators.get("adx")
|
|
||||||
if adx_sig is not None:
|
|
||||||
adx_val = float(adx_sig.value)
|
|
||||||
atr_pct = float(analysis.key_levels.get("atr_pct", 0.0))
|
|
||||||
rsi_sig = analysis.indicators.get("rsi")
|
|
||||||
if rsi_sig is not None:
|
|
||||||
rsi_val = float(rsi_sig.value)
|
|
||||||
ema_trend = analysis.key_levels.get("ema_trend")
|
|
||||||
if ema_trend and ema_trend > 0:
|
|
||||||
ema_dist = (raw_entry - ema_trend) / ema_trend * 100.0
|
|
||||||
|
|
||||||
# Per-timeframe scores from MTF map (keys are tf.value strings)
|
|
||||||
m15_s = float(report.mtf_scores.get("15min", 0.0))
|
|
||||||
h1_s = float(report.mtf_scores.get("1h", 0.0))
|
|
||||||
h4_s = float(report.mtf_scores.get("4h", 0.0))
|
|
||||||
|
|
||||||
entry_ts = next_bar.name
|
|
||||||
entry_hour = int(entry_ts.hour) if hasattr(entry_ts, "hour") else -1
|
|
||||||
entry_dow = int(entry_ts.dayofweek) if hasattr(entry_ts, "dayofweek") else -1
|
|
||||||
|
|
||||||
return {
|
|
||||||
"direction": direction,
|
|
||||||
"entry_time": next_bar.name,
|
|
||||||
"entry_price": entry_price,
|
|
||||||
"stop_loss": sl,
|
|
||||||
"take_profit": tp,
|
|
||||||
"risk_pips": risk_pips,
|
|
||||||
"pip_currency_value": pip_currency_value,
|
|
||||||
"balance_at_open": balance,
|
|
||||||
"grade": report.signal_quality,
|
|
||||||
"score_at_entry": float(report.final_score),
|
|
||||||
"confidence_at_entry": float(report.final_confidence),
|
|
||||||
"bar_index_open": next_bar.name,
|
|
||||||
# Diagnostics
|
|
||||||
"entry_hour": entry_hour,
|
|
||||||
"entry_dow": entry_dow,
|
|
||||||
"adx_at_entry": adx_val,
|
|
||||||
"atr_pct_at_entry": atr_pct,
|
|
||||||
"market_regime": report.market_regime or "",
|
|
||||||
"volatility_regime": report.volatility_regime or "",
|
|
||||||
"mtf_agreement": bool(report.mtf_agreement),
|
|
||||||
"m15_score": m15_s,
|
|
||||||
"h1_score": h1_s,
|
|
||||||
"h4_score": h4_s,
|
|
||||||
"rsi_at_entry": rsi_val,
|
|
||||||
"ema_distance_pct": ema_dist,
|
|
||||||
}
|
|
||||||
|
|
||||||
def _check_exit(
|
|
||||||
self, position: Dict, bar: pd.Series
|
|
||||||
) -> Optional[Dict]:
|
|
||||||
"""Return {price, reason} if SL or TP hit, else None.
|
|
||||||
|
|
||||||
On the entry bar (bar with same timestamp as position's entry_time),
|
|
||||||
we already filled at the open — only check subsequent bars' OHLC.
|
|
||||||
"""
|
|
||||||
if bar.name == position["bar_index_open"]:
|
|
||||||
return None # entry bar — no exit check
|
|
||||||
|
|
||||||
bt = self.config.backtest
|
|
||||||
high = float(bar["High"])
|
|
||||||
low = float(bar["Low"])
|
|
||||||
|
|
||||||
sl = position["stop_loss"]
|
|
||||||
tp = position["take_profit"]
|
|
||||||
slip = bt.slippage_pips * PIP_VALUE
|
|
||||||
half_spread = (0.5 if bt.use_spread_cost else 0.0) * PIP_VALUE
|
|
||||||
|
|
||||||
if position["direction"] == "BUY":
|
|
||||||
sl_hit = low <= sl
|
|
||||||
tp_hit = high >= tp
|
|
||||||
if sl_hit and tp_hit:
|
|
||||||
# Conservative: SL first
|
|
||||||
return {"price": sl - slip - half_spread, "reason": "sl"} if bt.sl_first_on_tie else \
|
|
||||||
{"price": tp - slip - half_spread, "reason": "tp"}
|
|
||||||
if sl_hit:
|
|
||||||
return {"price": sl - slip - half_spread, "reason": "sl"}
|
|
||||||
if tp_hit:
|
|
||||||
return {"price": tp - slip - half_spread, "reason": "tp"}
|
|
||||||
else: # SELL
|
|
||||||
sl_hit = high >= sl
|
|
||||||
tp_hit = low <= tp
|
|
||||||
if sl_hit and tp_hit:
|
|
||||||
return {"price": sl + slip + half_spread, "reason": "sl"} if bt.sl_first_on_tie else \
|
|
||||||
{"price": tp + slip + half_spread, "reason": "tp"}
|
|
||||||
if sl_hit:
|
|
||||||
return {"price": sl + slip + half_spread, "reason": "sl"}
|
|
||||||
if tp_hit:
|
|
||||||
return {"price": tp + slip + half_spread, "reason": "tp"}
|
|
||||||
return None
|
|
||||||
|
|
||||||
def _close_trade(
|
|
||||||
self, position: Dict, exit_time: pd.Timestamp, exit_info: Dict, balance: float
|
|
||||||
) -> Trade:
|
|
||||||
exit_price = exit_info["price"]
|
|
||||||
reason = exit_info["reason"]
|
|
||||||
|
|
||||||
if position["direction"] == "BUY":
|
|
||||||
pnl_pips = (exit_price - position["entry_price"]) / PIP_VALUE
|
|
||||||
else:
|
|
||||||
pnl_pips = (position["entry_price"] - exit_price) / PIP_VALUE
|
|
||||||
|
|
||||||
pnl_currency = pnl_pips * position["pip_currency_value"]
|
|
||||||
pnl_pct = pnl_currency / position["balance_at_open"] * 100
|
|
||||||
|
|
||||||
# Duration in primary-TF bars (timestamp diff / period)
|
|
||||||
period_min = TF_MINUTES[self.config.primary_timeframe]
|
|
||||||
duration_minutes = (exit_time - position["entry_time"]).total_seconds() / 60
|
|
||||||
duration_bars = int(round(duration_minutes / period_min))
|
|
||||||
|
|
||||||
return Trade(
|
|
||||||
entry_time=position["entry_time"],
|
|
||||||
exit_time=exit_time,
|
|
||||||
direction=position["direction"],
|
|
||||||
entry_price=position["entry_price"],
|
|
||||||
exit_price=exit_price,
|
|
||||||
stop_loss=position["stop_loss"],
|
|
||||||
take_profit=position["take_profit"],
|
|
||||||
exit_reason=reason,
|
|
||||||
risk_pips=position["risk_pips"],
|
|
||||||
pnl_pips=pnl_pips,
|
|
||||||
pnl_pct=pnl_pct,
|
|
||||||
pnl_currency=pnl_currency,
|
|
||||||
duration_bars=duration_bars,
|
|
||||||
grade=position["grade"],
|
|
||||||
score_at_entry=position["score_at_entry"],
|
|
||||||
confidence_at_entry=position["confidence_at_entry"],
|
|
||||||
entry_hour=position.get("entry_hour", -1),
|
|
||||||
entry_dow=position.get("entry_dow", -1),
|
|
||||||
adx_at_entry=position.get("adx_at_entry", 0.0),
|
|
||||||
atr_pct_at_entry=position.get("atr_pct_at_entry", 0.0),
|
|
||||||
market_regime=position.get("market_regime", ""),
|
|
||||||
volatility_regime=position.get("volatility_regime", ""),
|
|
||||||
mtf_agreement=position.get("mtf_agreement", False),
|
|
||||||
m15_score=position.get("m15_score", 0.0),
|
|
||||||
h1_score=position.get("h1_score", 0.0),
|
|
||||||
h4_score=position.get("h4_score", 0.0),
|
|
||||||
rsi_at_entry=position.get("rsi_at_entry", 50.0),
|
|
||||||
ema_distance_pct=position.get("ema_distance_pct", 0.0),
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# ══════════════════════════════════════════════════════════════════
|
|
||||||
# PARAMETER OPTIMIZER (PARALLEL)
|
|
||||||
# ══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
def _apply_overrides(config: PlatformConfig, overrides: Dict[str, float]) -> PlatformConfig:
|
|
||||||
"""Return a deep-copied config with parameter overrides applied."""
|
|
||||||
new_config = copy.deepcopy(config)
|
|
||||||
|
|
||||||
if "atr_sl_multiplier" in overrides:
|
|
||||||
new_config.signals.atr_sl_multiplier = float(overrides["atr_sl_multiplier"])
|
|
||||||
if "default_rr_ratio" in overrides:
|
|
||||||
new_config.signals.default_rr_ratio = float(overrides["default_rr_ratio"])
|
|
||||||
if "min_trade_grade" in overrides:
|
|
||||||
new_config.backtest.min_trade_grade = str(overrides["min_trade_grade"])
|
|
||||||
if "min_abs_score" in overrides:
|
|
||||||
new_config.backtest.min_abs_score = float(overrides["min_abs_score"])
|
|
||||||
if "invert_signal" in overrides:
|
|
||||||
new_config.backtest.invert_signal = bool(overrides["invert_signal"])
|
|
||||||
if "regime_filter" in overrides:
|
|
||||||
new_config.backtest.regime_filter = bool(overrides["regime_filter"])
|
|
||||||
if "regime_filter_min_adx" in overrides:
|
|
||||||
new_config.backtest.regime_filter_min_adx = float(overrides["regime_filter_min_adx"])
|
|
||||||
if "session_filter" in overrides:
|
|
||||||
new_config.backtest.session_filter = bool(overrides["session_filter"])
|
|
||||||
if "session_start_hour" in overrides:
|
|
||||||
new_config.backtest.session_start_hour = int(overrides["session_start_hour"])
|
|
||||||
if "session_end_hour" in overrides:
|
|
||||||
new_config.backtest.session_end_hour = int(overrides["session_end_hour"])
|
|
||||||
if "auto_session_filter" in overrides:
|
|
||||||
new_config.backtest.auto_session_filter = bool(overrides["auto_session_filter"])
|
|
||||||
if "auto_session_lookback_trades" in overrides:
|
|
||||||
new_config.backtest.auto_session_lookback_trades = int(overrides["auto_session_lookback_trades"])
|
|
||||||
if "auto_session_threshold_pct" in overrides:
|
|
||||||
new_config.backtest.auto_session_threshold_pct = float(overrides["auto_session_threshold_pct"])
|
|
||||||
if "risk_per_trade_pct" in overrides:
|
|
||||||
new_config.backtest.risk_per_trade_pct = float(overrides["risk_per_trade_pct"])
|
|
||||||
if "disable_indicator" in overrides:
|
|
||||||
v = overrides["disable_indicator"]
|
|
||||||
new_config.technical.disabled_indicators = [str(v)] if v else []
|
|
||||||
|
|
||||||
return new_config
|
|
||||||
|
|
||||||
|
|
||||||
def _run_single_combo(args: Tuple[PlatformConfig, Dict[str, float]]) -> BacktestResult:
|
|
||||||
"""Worker function — must be top-level for multiprocessing.Pool."""
|
|
||||||
base_config, overrides = args
|
|
||||||
config = _apply_overrides(base_config, overrides)
|
|
||||||
backtester = WalkForwardBacktester(config)
|
|
||||||
# Workers reuse the disk parquet cache, so no Twelve Data fetch needed
|
|
||||||
result = backtester.run(progress=False)
|
|
||||||
result.params = overrides
|
|
||||||
return result
|
|
||||||
|
|
||||||
|
|
||||||
class Optimizer:
|
|
||||||
"""Embarrassingly parallel grid search over BacktestConfig.param_grid."""
|
|
||||||
|
|
||||||
def __init__(self, config: PlatformConfig):
|
|
||||||
self.config = config
|
|
||||||
|
|
||||||
def run_grid(
|
|
||||||
self,
|
|
||||||
param_grid: Optional[Dict[str, List[float]]] = None,
|
|
||||||
prewarm_cache: bool = True,
|
|
||||||
) -> List[BacktestResult]:
|
|
||||||
if param_grid is None:
|
|
||||||
param_grid = self.config.backtest.param_grid
|
|
||||||
|
|
||||||
# Prewarm the disk cache once before fanning out — avoids each worker
|
|
||||||
# re-fetching from Twelve Data.
|
|
||||||
if prewarm_cache:
|
|
||||||
logger.info("Prewarming disk cache for backtest data...")
|
|
||||||
warmer = WalkForwardBacktester(self.config)
|
|
||||||
warmer.load_data()
|
|
||||||
|
|
||||||
keys = list(param_grid.keys())
|
|
||||||
value_lists = [param_grid[k] for k in keys]
|
|
||||||
combos = [
|
|
||||||
dict(zip(keys, vals))
|
|
||||||
for vals in itertools.product(*value_lists)
|
|
||||||
]
|
|
||||||
|
|
||||||
n_workers = min(
|
|
||||||
self.config.backtest.parallel_workers,
|
|
||||||
len(combos),
|
|
||||||
os.cpu_count() or 1,
|
|
||||||
)
|
|
||||||
logger.info(
|
|
||||||
f"Optimizer: {len(combos)} combos × {n_workers} workers"
|
|
||||||
)
|
|
||||||
|
|
||||||
tasks = [(self.config, c) for c in combos]
|
|
||||||
|
|
||||||
if n_workers <= 1:
|
|
||||||
results = [_run_single_combo(t) for t in tasks]
|
|
||||||
else:
|
|
||||||
with mp.Pool(processes=n_workers) as pool:
|
|
||||||
results = pool.map(_run_single_combo, tasks)
|
|
||||||
|
|
||||||
# Sort by total return descending
|
|
||||||
results.sort(
|
|
||||||
key=lambda r: r.metrics.get("total_return_pct", -float("inf")),
|
|
||||||
reverse=True,
|
|
||||||
)
|
|
||||||
return results
|
|
||||||
-1323
File diff suppressed because it is too large
Load Diff
@@ -1,15 +1,11 @@
|
|||||||
"""
|
"""
|
||||||
ForexBot2 — Configuration (USD/JPY focus)
|
ForexBot2 — Configuration (USD/JPY focus)
|
||||||
==========================================
|
==========================================
|
||||||
Single-pair signal & backtest configuration.
|
|
||||||
All knobs that govern indicator behavior, signal scoring, news handling,
|
|
||||||
and the walk-forward backtester live here.
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import os
|
|
||||||
from dataclasses import dataclass, field
|
from dataclasses import dataclass, field
|
||||||
from enum import Enum
|
from enum import Enum
|
||||||
from typing import Dict, List, Optional
|
from typing import Dict, List
|
||||||
|
|
||||||
|
|
||||||
class Timeframe(Enum):
|
class Timeframe(Enum):
|
||||||
@@ -85,23 +81,7 @@ class TechnicalConfig:
|
|||||||
pivot_lookback: int = 1
|
pivot_lookback: int = 1
|
||||||
divergence_lookback: int = 14
|
divergence_lookback: int = 14
|
||||||
|
|
||||||
# Ablation control — names listed here are skipped during analyze().
|
disabled_indicators: List[str] = field(default_factory=list)
|
||||||
# Use lowercase keys: ema_trend, rsi, macd, bollinger, stochastic,
|
|
||||||
# adx, ichimoku, divergence, candlestick, pivot, atr, volume.
|
|
||||||
#
|
|
||||||
# Defaults disable indicators that empirically hurt P&L on USD/JPY across
|
|
||||||
# 2024-trend / 2023-chop / 2026-recent ablation sweeps (see project memory):
|
|
||||||
# - pivot: net +9.5% to disable across 3 windows
|
|
||||||
# - candlestick: net +3.4% to disable
|
|
||||||
disabled_indicators: List[str] = field(
|
|
||||||
default_factory=lambda: ["pivot", "candlestick"]
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
ALL_INDICATORS: List[str] = [
|
|
||||||
"ema_trend", "rsi", "macd", "bollinger", "stochastic",
|
|
||||||
"adx", "ichimoku", "divergence", "candlestick", "pivot",
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
@@ -141,67 +121,15 @@ class NewsConfig:
|
|||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
class BacktestConfig:
|
class FilterConfig:
|
||||||
"""Walk-forward backtest configuration."""
|
"""Regime and session filters for live signals."""
|
||||||
# Data window — None means "all available history"
|
|
||||||
start_date: Optional[str] = None # "YYYY-MM-DD"
|
|
||||||
end_date: Optional[str] = None # "YYYY-MM-DD"
|
|
||||||
max_bars: int = 5000 # Twelve Data ceiling per request
|
|
||||||
|
|
||||||
# Account & sizing
|
|
||||||
initial_balance: float = 10_000.0
|
|
||||||
risk_per_trade_pct: float = 1.0 # % of equity risked per trade
|
|
||||||
|
|
||||||
# Execution realism
|
|
||||||
use_spread_cost: bool = True
|
|
||||||
slippage_pips: float = 0.5 # added to spread on entry & exit
|
|
||||||
one_position_only: bool = True
|
|
||||||
sl_first_on_tie: bool = True # if SL & TP both inside bar, SL hits first
|
|
||||||
|
|
||||||
# Signal gates
|
|
||||||
min_trade_grade: str = "B" # "A+", "A", "B", "C", "D", "F"
|
|
||||||
min_abs_score: float = 0.0 # |final_score| must exceed this to enter
|
|
||||||
|
|
||||||
# Sanity-test knob — flip BUY↔SELL to see if the engine has reversed edge
|
|
||||||
invert_signal: bool = False
|
|
||||||
|
|
||||||
# Regime gate — only trade when a higher-TF trend is confirmed
|
|
||||||
regime_filter: bool = False
|
regime_filter: bool = False
|
||||||
regime_filter_timeframe: Timeframe = Timeframe.H4
|
regime_filter_timeframe: Timeframe = Timeframe.H4
|
||||||
regime_filter_min_adx: float = 25.0
|
regime_filter_min_adx: float = 25.0
|
||||||
|
|
||||||
# Session gate — only trade during a specified UTC hour window.
|
|
||||||
# 2026 diagnostic backtest showed 07-15 UTC (London + NY) had
|
|
||||||
# 47% WR / +127 pips while off-hours were 0% WR / -571 pips.
|
|
||||||
session_filter: bool = False
|
session_filter: bool = False
|
||||||
session_start_hour: int = 7 # UTC, inclusive
|
session_start_hour: int = 7
|
||||||
session_end_hour: int = 16 # UTC, exclusive — so trades fire on 07..15
|
session_end_hour: int = 16
|
||||||
|
|
||||||
# Auto-toggle: enable session filter ONLY when recent realised trades
|
|
||||||
# show adverse expectancy (mean pnl_pct over the last N closed trades
|
|
||||||
# below threshold). Releases automatically once expectancy recovers.
|
|
||||||
# Designed so trending regimes keep all hours, while adverse/chop
|
|
||||||
# regimes auto-restrict to liquid sessions.
|
|
||||||
auto_session_filter: bool = False
|
|
||||||
auto_session_lookback_trades: int = 10
|
|
||||||
auto_session_threshold_pct: float = -0.3
|
|
||||||
|
|
||||||
# Parallelism (used by Optimizer; single-run backtest is sequential)
|
|
||||||
parallel_workers: int = field(default_factory=lambda: os.cpu_count() or 4)
|
|
||||||
|
|
||||||
# Optimizer
|
|
||||||
optimize: bool = False
|
|
||||||
param_grid: Dict[str, list] = field(default_factory=lambda: {
|
|
||||||
# 2 × 2 × 3 × 2 × 2 = 48 combos
|
|
||||||
"atr_sl_multiplier": [1.5, 2.5],
|
|
||||||
"default_rr_ratio": [1.5, 2.5],
|
|
||||||
"min_abs_score": [15.0, 35.0, 60.0],
|
|
||||||
"min_trade_grade": ["B", "A"],
|
|
||||||
"invert_signal": [False, True],
|
|
||||||
})
|
|
||||||
|
|
||||||
# Cache directory for fetched OHLCV (parquet)
|
|
||||||
cache_dir: str = ".cache"
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
@@ -210,7 +138,7 @@ class PlatformConfig:
|
|||||||
technical: TechnicalConfig = field(default_factory=TechnicalConfig)
|
technical: TechnicalConfig = field(default_factory=TechnicalConfig)
|
||||||
signals: SignalConfig = field(default_factory=SignalConfig)
|
signals: SignalConfig = field(default_factory=SignalConfig)
|
||||||
news: NewsConfig = field(default_factory=NewsConfig)
|
news: NewsConfig = field(default_factory=NewsConfig)
|
||||||
backtest: BacktestConfig = field(default_factory=BacktestConfig)
|
backtest: FilterConfig = field(default_factory=FilterConfig)
|
||||||
|
|
||||||
symbol: str = USDJPY
|
symbol: str = USDJPY
|
||||||
display_name: str = DISPLAY
|
display_name: str = DISPLAY
|
||||||
@@ -221,12 +149,10 @@ class PlatformConfig:
|
|||||||
default_factory=lambda: [Timeframe.M15, Timeframe.H4]
|
default_factory=lambda: [Timeframe.M15, Timeframe.H4]
|
||||||
)
|
)
|
||||||
|
|
||||||
data_lookback_days: int = 60 # enough for 200 EMA on H1
|
data_lookback_days: int = 60
|
||||||
|
|
||||||
twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
|
twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
|
||||||
|
|
||||||
refresh_interval: int = 300 # 5 min cache TTL
|
refresh_interval: int = 300
|
||||||
|
|
||||||
log_level: str = "INFO"
|
log_level: str = "INFO"
|
||||||
log_signals_to_file: bool = True
|
|
||||||
signal_log_path: str = "signals.log"
|
|
||||||
|
|||||||
@@ -2,9 +2,6 @@
|
|||||||
ForexBot2 — Data Provider (Twelve Data SDK, USD/JPY only)
|
ForexBot2 — Data Provider (Twelve Data SDK, USD/JPY only)
|
||||||
==========================================================
|
==========================================================
|
||||||
Fetches and validates OHLCV via the official `twelvedata` Python SDK.
|
Fetches and validates OHLCV via the official `twelvedata` Python SDK.
|
||||||
No synthetic fallback — if the API fails the caller learns about it.
|
|
||||||
Adds a parquet on-disk cache so the backtest worker pool reuses one
|
|
||||||
download instead of hammering Twelve Data during a parameter sweep.
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import logging
|
import logging
|
||||||
@@ -29,14 +26,6 @@ class DataIntegrityError(Exception):
|
|||||||
class ForexDataProvider:
|
class ForexDataProvider:
|
||||||
"""Twelve Data SDK client + on-disk parquet cache."""
|
"""Twelve Data SDK client + on-disk parquet cache."""
|
||||||
|
|
||||||
# Local MetaTrader CSV exports — tab-delimited, 7 columns (includes Spread)
|
|
||||||
LOCAL_CSV_MAP = {
|
|
||||||
Timeframe.M15: "USDJPY_M15.csv",
|
|
||||||
Timeframe.H1: "USDJPY_H1.csv",
|
|
||||||
Timeframe.H4: "USDJPY_H4.csv",
|
|
||||||
Timeframe.D1: "USDJPY_D1.csv",
|
|
||||||
}
|
|
||||||
|
|
||||||
TD_INTERVALS = {
|
TD_INTERVALS = {
|
||||||
Timeframe.M1: "1min",
|
Timeframe.M1: "1min",
|
||||||
Timeframe.M5: "5min",
|
Timeframe.M5: "5min",
|
||||||
@@ -48,18 +37,6 @@ class ForexDataProvider:
|
|||||||
Timeframe.W1: "1week",
|
Timeframe.W1: "1week",
|
||||||
}
|
}
|
||||||
|
|
||||||
BARS_PER_DAY = {
|
|
||||||
Timeframe.M1: 1440,
|
|
||||||
Timeframe.M5: 288,
|
|
||||||
Timeframe.M15: 96,
|
|
||||||
Timeframe.M30: 48,
|
|
||||||
Timeframe.H1: 24,
|
|
||||||
Timeframe.H4: 6,
|
|
||||||
Timeframe.D1: 1,
|
|
||||||
Timeframe.W1: 0.143,
|
|
||||||
}
|
|
||||||
|
|
||||||
# Free plan: 8 credits / minute
|
|
||||||
RATE_LIMIT = 8
|
RATE_LIMIT = 8
|
||||||
RATE_WINDOW = 60.0
|
RATE_WINDOW = 60.0
|
||||||
|
|
||||||
@@ -116,100 +93,6 @@ class ForexDataProvider:
|
|||||||
self._cache_timestamps[cache_key] = now
|
self._cache_timestamps[cache_key] = now
|
||||||
return df
|
return df
|
||||||
|
|
||||||
def fetch_full_history(
|
|
||||||
self,
|
|
||||||
symbol: str = USDJPY,
|
|
||||||
timeframe: Timeframe = Timeframe.H1,
|
|
||||||
max_bars: int = 5000,
|
|
||||||
use_disk_cache: bool = True,
|
|
||||||
start_date: Optional[str] = None,
|
|
||||||
end_date: Optional[str] = None,
|
|
||||||
) -> pd.DataFrame:
|
|
||||||
"""
|
|
||||||
Pull the largest window Twelve Data allows in one call.
|
|
||||||
On-disk parquet cache prevents re-download across worker subprocesses
|
|
||||||
during an optimizer sweep.
|
|
||||||
"""
|
|
||||||
cache_path = self._disk_cache_path(symbol, timeframe, start_date, end_date)
|
|
||||||
|
|
||||||
if use_disk_cache and os.path.exists(cache_path):
|
|
||||||
try:
|
|
||||||
df = pd.read_parquet(cache_path)
|
|
||||||
if df.index.tz is None:
|
|
||||||
df.index = df.index.tz_localize("UTC")
|
|
||||||
logger.info(
|
|
||||||
f"Loaded {len(df)} bars from disk cache: {cache_path}"
|
|
||||||
)
|
|
||||||
return df
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Disk cache read failed ({e}); refetching.")
|
|
||||||
|
|
||||||
outputsize = min(max_bars, 5000)
|
|
||||||
df = self._fetch_twelve_data(
|
|
||||||
symbol, timeframe, outputsize,
|
|
||||||
start_date=start_date, end_date=end_date,
|
|
||||||
)
|
|
||||||
df = self._validate_and_clean(df, symbol, timeframe)
|
|
||||||
|
|
||||||
if use_disk_cache:
|
|
||||||
os.makedirs(os.path.dirname(cache_path), exist_ok=True)
|
|
||||||
try:
|
|
||||||
df.to_parquet(cache_path)
|
|
||||||
logger.info(f"Cached {len(df)} bars → {cache_path}")
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Disk cache write failed: {e}")
|
|
||||||
|
|
||||||
return df
|
|
||||||
|
|
||||||
def load_local_csv(
|
|
||||||
self,
|
|
||||||
timeframe: Timeframe,
|
|
||||||
data_dir: str = "market_data",
|
|
||||||
start_date: Optional[str] = None,
|
|
||||||
end_date: Optional[str] = None,
|
|
||||||
) -> Optional[pd.DataFrame]:
|
|
||||||
"""
|
|
||||||
Load OHLCV from a local MetaTrader CSV export.
|
|
||||||
Format: tab-separated, columns Time/Open/High/Low/Close/Volume/Spread.
|
|
||||||
Returns None silently if the file doesn't exist for this timeframe.
|
|
||||||
"""
|
|
||||||
fname = self.LOCAL_CSV_MAP.get(timeframe)
|
|
||||||
if fname is None:
|
|
||||||
logger.info(f"No local CSV mapping for {timeframe.value} — skipping")
|
|
||||||
return None
|
|
||||||
|
|
||||||
path = os.path.join(data_dir, fname)
|
|
||||||
if not os.path.exists(path):
|
|
||||||
logger.warning(f"Local CSV not found: {path}")
|
|
||||||
return None
|
|
||||||
|
|
||||||
df = pd.read_csv(
|
|
||||||
path, sep="\t",
|
|
||||||
names=["Time", "Open", "High", "Low", "Close", "Volume", "Spread"],
|
|
||||||
header=0,
|
|
||||||
parse_dates=["Time"],
|
|
||||||
)
|
|
||||||
df = df.set_index("Time")
|
|
||||||
df = df.drop(columns=["Spread"], errors="ignore")
|
|
||||||
for col in ("Open", "High", "Low", "Close", "Volume"):
|
|
||||||
if col in df.columns:
|
|
||||||
df[col] = pd.to_numeric(df[col], errors="coerce")
|
|
||||||
if "Volume" not in df.columns:
|
|
||||||
df["Volume"] = 0.0
|
|
||||||
|
|
||||||
if df.index.tz is None:
|
|
||||||
df.index = df.index.tz_localize("UTC")
|
|
||||||
df.index.name = "Datetime"
|
|
||||||
|
|
||||||
if start_date:
|
|
||||||
df = df[df.index >= pd.Timestamp(start_date, tz="UTC")]
|
|
||||||
if end_date:
|
|
||||||
df = df[df.index <= pd.Timestamp(end_date, tz="UTC")]
|
|
||||||
|
|
||||||
df = self._validate_and_clean(df, USDJPY, timeframe)
|
|
||||||
logger.info(f"Loaded {len(df):,} bars from local CSV: {path}")
|
|
||||||
return df
|
|
||||||
|
|
||||||
def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
|
def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
|
||||||
try:
|
try:
|
||||||
df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
|
df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
|
||||||
@@ -240,22 +123,6 @@ class ForexDataProvider:
|
|||||||
|
|
||||||
# ── Private ────────────────────────────────────────────────────
|
# ── Private ────────────────────────────────────────────────────
|
||||||
|
|
||||||
def _disk_cache_path(
|
|
||||||
self,
|
|
||||||
symbol: str,
|
|
||||||
timeframe: Timeframe,
|
|
||||||
start_date: Optional[str] = None,
|
|
||||||
end_date: Optional[str] = None,
|
|
||||||
) -> str:
|
|
||||||
clean = symbol.replace("=X", "").lower()
|
|
||||||
suffix = ""
|
|
||||||
if start_date or end_date:
|
|
||||||
suffix = f"_{start_date or 'min'}_{end_date or 'max'}"
|
|
||||||
return os.path.join(
|
|
||||||
self.config.backtest.cache_dir,
|
|
||||||
f"{clean}_{timeframe.value}{suffix}.parquet",
|
|
||||||
)
|
|
||||||
|
|
||||||
def _throttle(self, credits: int = 1) -> None:
|
def _throttle(self, credits: int = 1) -> None:
|
||||||
"""Free tier: 8 credits / minute. Block until budget allows."""
|
"""Free tier: 8 credits / minute. Block until budget allows."""
|
||||||
while True:
|
while True:
|
||||||
|
|||||||
@@ -171,7 +171,7 @@ class TechnicalEngine:
|
|||||||
result.indicators["ema_trend"] = IndicatorSignal(
|
result.indicators["ema_trend"] = IndicatorSignal(
|
||||||
name="EMA Trend", value=normalized,
|
name="EMA Trend", value=normalized,
|
||||||
signal=signal, confidence=conf,
|
signal=signal, confidence=conf,
|
||||||
description=desc, weight=1.8,
|
description=desc, weight=1.0,
|
||||||
)
|
)
|
||||||
if t is not None:
|
if t is not None:
|
||||||
result.key_levels["ema_trend"] = float(t)
|
result.key_levels["ema_trend"] = float(t)
|
||||||
@@ -222,7 +222,7 @@ class TechnicalEngine:
|
|||||||
|
|
||||||
result.indicators["rsi"] = IndicatorSignal(
|
result.indicators["rsi"] = IndicatorSignal(
|
||||||
name="RSI", value=rsi_val, signal=signal,
|
name="RSI", value=rsi_val, signal=signal,
|
||||||
confidence=conf, description=desc, weight=0.0,
|
confidence=conf, description=desc, weight=1.8,
|
||||||
)
|
)
|
||||||
|
|
||||||
# Attach RSI series for divergence detection (dynamic attribute, filtered by _prefix)
|
# Attach RSI series for divergence detection (dynamic attribute, filtered by _prefix)
|
||||||
@@ -291,7 +291,7 @@ class TechnicalEngine:
|
|||||||
|
|
||||||
result.indicators["macd"] = IndicatorSignal(
|
result.indicators["macd"] = IndicatorSignal(
|
||||||
name="MACD", value=hist_val, signal=signal,
|
name="MACD", value=hist_val, signal=signal,
|
||||||
confidence=conf, description=desc, weight=3.3,
|
confidence=conf, description=desc, weight=1.2,
|
||||||
)
|
)
|
||||||
|
|
||||||
# Attach histogram for divergence detection
|
# Attach histogram for divergence detection
|
||||||
@@ -358,7 +358,7 @@ class TechnicalEngine:
|
|||||||
|
|
||||||
result.indicators["bollinger"] = IndicatorSignal(
|
result.indicators["bollinger"] = IndicatorSignal(
|
||||||
name="Bollinger Bands", value=bb_position, signal=signal,
|
name="Bollinger Bands", value=bb_position, signal=signal,
|
||||||
confidence=conf, description=desc, weight=0.0,
|
confidence=conf, description=desc, weight=1.5,
|
||||||
)
|
)
|
||||||
|
|
||||||
result.key_levels["bb_upper"] = upper_val
|
result.key_levels["bb_upper"] = upper_val
|
||||||
@@ -423,7 +423,7 @@ class TechnicalEngine:
|
|||||||
|
|
||||||
result.indicators["stochastic"] = IndicatorSignal(
|
result.indicators["stochastic"] = IndicatorSignal(
|
||||||
name="Stochastic", value=k_val, signal=signal,
|
name="Stochastic", value=k_val, signal=signal,
|
||||||
confidence=conf, description=desc, weight=2.4,
|
confidence=conf, description=desc, weight=2.5,
|
||||||
)
|
)
|
||||||
|
|
||||||
def _compute_adx(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
def _compute_adx(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||||||
@@ -609,7 +609,7 @@ class TechnicalEngine:
|
|||||||
name="Ichimoku", value=float(score), signal=signal,
|
name="Ichimoku", value=float(score), signal=signal,
|
||||||
confidence=conf,
|
confidence=conf,
|
||||||
description=f"Ichimoku [{score}/4]: {' | '.join(signals)}",
|
description=f"Ichimoku [{score}/4]: {' | '.join(signals)}",
|
||||||
weight=0.55,
|
weight=0.3,
|
||||||
)
|
)
|
||||||
|
|
||||||
result.key_levels["ichimoku_cloud_top"] = cloud_top
|
result.key_levels["ichimoku_cloud_top"] = cloud_top
|
||||||
@@ -672,7 +672,7 @@ class TechnicalEngine:
|
|||||||
name="Divergence", value=len(divergences),
|
name="Divergence", value=len(divergences),
|
||||||
signal=signal, confidence=70,
|
signal=signal, confidence=70,
|
||||||
description=" | ".join(divergences),
|
description=" | ".join(divergences),
|
||||||
weight=1.5, # divergences are strong reversal signals
|
weight=3.5,
|
||||||
)
|
)
|
||||||
# Note: divergences are NOT added to pattern_signals to avoid
|
# Note: divergences are NOT added to pattern_signals to avoid
|
||||||
# duplicate reason collection (the indicator entry covers them).
|
# duplicate reason collection (the indicator entry covers them).
|
||||||
@@ -742,7 +742,7 @@ class TechnicalEngine:
|
|||||||
name="Candlestick", value=float(bullish - bearish),
|
name="Candlestick", value=float(bullish - bearish),
|
||||||
signal=signal, confidence=50,
|
signal=signal, confidence=50,
|
||||||
description=" | ".join(patterns),
|
description=" | ".join(patterns),
|
||||||
weight=0.7,
|
weight=1.2,
|
||||||
)
|
)
|
||||||
result.pattern_signals.extend(patterns)
|
result.pattern_signals.extend(patterns)
|
||||||
|
|
||||||
@@ -784,7 +784,7 @@ class TechnicalEngine:
|
|||||||
result.indicators["pivot"] = IndicatorSignal(
|
result.indicators["pivot"] = IndicatorSignal(
|
||||||
name="Pivot Points", value=pivot,
|
name="Pivot Points", value=pivot,
|
||||||
signal=SignalStrength.BUY if latest > pivot else SignalStrength.SELL,
|
signal=SignalStrength.BUY if latest > pivot else SignalStrength.SELL,
|
||||||
confidence=45, description=desc, weight=0.6,
|
confidence=45, description=desc, weight=0.8,
|
||||||
)
|
)
|
||||||
|
|
||||||
def _compute_volume_analysis(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
def _compute_volume_analysis(self, df: pd.DataFrame, result: TechnicalAnalysis):
|
||||||
@@ -854,12 +854,10 @@ class TechnicalEngine:
|
|||||||
total_weight = 0.0
|
total_weight = 0.0
|
||||||
total_confidence = 0.0
|
total_confidence = 0.0
|
||||||
|
|
||||||
# Regime-gate ichimoku: trend hero (-23% to disable in 2024 trend) but
|
|
||||||
# chop villain (+18% to disable in 2023 chop & 2026 recent).
|
|
||||||
regime_weights = {
|
regime_weights = {
|
||||||
"trending_up": {"ema_trend": 1.5, "adx": 1.3, "macd": 1.2, "ichimoku": 1.3, "rsi": 0.7, "bollinger": 0.6},
|
"trending_up": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
|
||||||
"trending_down": {"ema_trend": 1.5, "adx": 1.3, "macd": 1.2, "ichimoku": 1.3, "rsi": 0.7, "bollinger": 0.6},
|
"trending_down": {"divergence": 1.5, "rsi": 1.2, "stochastic": 1.1, "candlestick": 1.2, "ema_trend": 1.0, "macd": 1.0, "adx": 1.0, "ichimoku": 0.8, "bollinger": 0.7, "pivot": 0.8},
|
||||||
"ranging": {"rsi": 1.4, "stochastic": 1.3, "bollinger": 1.4, "ema_trend": 0.6, "adx": 0.5, "ichimoku": 0.2},
|
"ranging": {"divergence": 1.3, "rsi": 1.4, "stochastic": 1.4, "bollinger": 1.5, "candlestick": 1.3, "pivot": 1.3, "ema_trend": 0.5, "macd": 0.6, "adx": 0.4, "ichimoku": 0.2},
|
||||||
}
|
}
|
||||||
|
|
||||||
active_regime_weights = regime_weights.get(result.market_regime, {})
|
active_regime_weights = regime_weights.get(result.market_regime, {})
|
||||||
|
|||||||
@@ -2,14 +2,10 @@
|
|||||||
"""
|
"""
|
||||||
ForexBot2 — USD/JPY Signal Intelligence (single-pair)
|
ForexBot2 — USD/JPY Signal Intelligence (single-pair)
|
||||||
=======================================================
|
=======================================================
|
||||||
Entry point for both live signal analysis and walk-forward backtesting.
|
|
||||||
|
|
||||||
Usage:
|
Usage:
|
||||||
python main.py # Live signal on H1
|
python main.py # Live signal on H1
|
||||||
python main.py --timeframe H4 --detail # Detailed H4 signal
|
python main.py --timeframe H4 --detail # Detailed H4 signal
|
||||||
python main.py --json # JSON output
|
python main.py --json # JSON output
|
||||||
python main.py --backtest # Walk-forward backtest
|
|
||||||
python main.py --backtest --optimize # Parallel grid search
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
@@ -233,54 +229,6 @@ def report_to_dict(report: SignalReport) -> dict:
|
|||||||
return d
|
return d
|
||||||
|
|
||||||
|
|
||||||
# ── Backtest renderer ──────────────────────────────────────────────
|
|
||||||
|
|
||||||
def print_backtest_report(result, params: dict = None, verbose_journal: bool = False):
|
|
||||||
m = result.metrics
|
|
||||||
print(f" {Colors.BOLD}{Colors.WHITE}Backtest Summary — USD/JPY{Colors.RESET}")
|
|
||||||
if params:
|
|
||||||
params_str = ", ".join(f"{k}={v}" for k, v in params.items())
|
|
||||||
print(f" {Colors.GRAY}Params: {params_str}{Colors.RESET}")
|
|
||||||
print(f" {Colors.GRAY}{'─' * 76}{Colors.RESET}")
|
|
||||||
|
|
||||||
initial = result.initial_balance
|
|
||||||
final = result.final_balance
|
|
||||||
ret_pct = m.get("total_return_pct", 0)
|
|
||||||
ret_color = Colors.GREEN if ret_pct >= 0 else Colors.RED
|
|
||||||
|
|
||||||
print(f" Trades: {Colors.BOLD}{m.get('total_trades', 0)}{Colors.RESET}"
|
|
||||||
f" ({m.get('wins', 0)}W / {m.get('losses', 0)}L)")
|
|
||||||
print(f" Win Rate: {m.get('win_rate', 0):.1f}%")
|
|
||||||
print(f" Profit Factor: {m.get('profit_factor', 0):.2f}")
|
|
||||||
pf_inf = m.get('profit_factor', 0) == float('inf')
|
|
||||||
if pf_inf:
|
|
||||||
print(f" (no losing trades — infinite PF)")
|
|
||||||
print(f" Expectancy: {m.get('expectancy_pips', 0):+.2f} pips/trade"
|
|
||||||
f" ({m.get('expectancy_per_trade', 0):+.2f} per trade)")
|
|
||||||
print(f" Initial Balance: ${initial:,.2f}")
|
|
||||||
print(f" Final Balance: {ret_color}${final:,.2f}{Colors.RESET}")
|
|
||||||
print(f" Total Return: {ret_color}{ret_pct:+.2f}%{Colors.RESET}")
|
|
||||||
print(f" CAGR: {m.get('cagr', 0):+.2f}%")
|
|
||||||
print(f" Max Drawdown: {Colors.RED}{m.get('max_drawdown_pct', 0):.2f}%{Colors.RESET}")
|
|
||||||
print(f" Sharpe Ratio: {m.get('sharpe', 0):.2f}")
|
|
||||||
print(f" Sortino Ratio: {m.get('sortino', 0):.2f}")
|
|
||||||
print(f" Avg Duration: {m.get('avg_trade_duration_bars', 0):.1f} bars")
|
|
||||||
print(f" Win Streak: {m.get('longest_winning_streak', 0)} | "
|
|
||||||
f"Loss Streak: {m.get('longest_losing_streak', 0)}")
|
|
||||||
print(f" {Colors.GRAY}{'─' * 76}{Colors.RESET}")
|
|
||||||
|
|
||||||
if verbose_journal and result.trades:
|
|
||||||
print(f"\n {Colors.BOLD}Trade Journal (last 10):{Colors.RESET}")
|
|
||||||
print(f" {'#':<4} {'Entry Time':<22} {'Dir':<5} {'Entry':<9} "
|
|
||||||
f"{'Exit':<9} {'Reason':<6} {'Pips':>8} {'P&L%':>7}")
|
|
||||||
for i, t in enumerate(result.trades[-10:], start=max(1, len(result.trades) - 9)):
|
|
||||||
color = Colors.GREEN if t.pnl_currency > 0 else Colors.RED
|
|
||||||
entry_str = str(t.entry_time)[:19]
|
|
||||||
print(f" {i:<4} {entry_str:<22} {t.direction:<5} {t.entry_price:<9.3f} "
|
|
||||||
f"{t.exit_price:<9.3f} {t.exit_reason:<6} "
|
|
||||||
f"{color}{t.pnl_pips:>+8.1f}{Colors.RESET} "
|
|
||||||
f"{color}{t.pnl_pct:>+6.2f}%{Colors.RESET}")
|
|
||||||
|
|
||||||
|
|
||||||
# ══════════════════════════════════════════════════════════════════
|
# ══════════════════════════════════════════════════════════════════
|
||||||
# CLI
|
# CLI
|
||||||
@@ -288,7 +236,7 @@ def print_backtest_report(result, params: dict = None, verbose_journal: bool = F
|
|||||||
|
|
||||||
def parse_args():
|
def parse_args():
|
||||||
parser = argparse.ArgumentParser(
|
parser = argparse.ArgumentParser(
|
||||||
description="ForexBot2 — USD/JPY Signal Intelligence + Backtester",
|
description="ForexBot2 — USD/JPY Signal Intelligence",
|
||||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||||
epilog="""
|
epilog="""
|
||||||
Examples:
|
Examples:
|
||||||
@@ -296,58 +244,18 @@ Examples:
|
|||||||
python main.py --detail Live signal with full breakdown
|
python main.py --detail Live signal with full breakdown
|
||||||
python main.py --timeframe H4 --detail H4 timeframe
|
python main.py --timeframe H4 --detail H4 timeframe
|
||||||
python main.py --json JSON output
|
python main.py --json JSON output
|
||||||
python main.py --backtest Walk-forward backtest (full history)
|
|
||||||
python main.py --backtest --bars 2000 Backtest on last 2000 bars
|
|
||||||
python main.py --backtest --optimize Parallel grid search across CPU cores
|
|
||||||
""",
|
""",
|
||||||
)
|
)
|
||||||
parser.add_argument("--timeframe", "-tf", default="H1",
|
parser.add_argument("--timeframe", "-tf", default="H1",
|
||||||
choices=["M5", "M15", "M30", "H1", "H4", "D1"],
|
choices=["M5", "M15", "M30", "H1", "H4", "D1"],
|
||||||
help="Primary analysis timeframe (default: H1)")
|
help="Primary analysis timeframe (default: H1)")
|
||||||
parser.add_argument("--detail", action="store_true",
|
parser.add_argument("--detail", action="store_true",
|
||||||
help="Show full indicator breakdown (live mode)")
|
help="Show full indicator breakdown")
|
||||||
parser.add_argument("--json", action="store_true",
|
parser.add_argument("--json", action="store_true",
|
||||||
help="Output as JSON")
|
help="Output as JSON")
|
||||||
parser.add_argument("--verbose", "-v", action="store_true",
|
parser.add_argument("--verbose", "-v", action="store_true",
|
||||||
help="Debug logging")
|
help="Debug logging")
|
||||||
|
|
||||||
# Backtest options
|
|
||||||
parser.add_argument("--backtest", action="store_true",
|
|
||||||
help="Run walk-forward backtest instead of live signal")
|
|
||||||
parser.add_argument("--optimize", action="store_true",
|
|
||||||
help="Run parallel parameter grid search (implies --backtest)")
|
|
||||||
parser.add_argument("--bars", type=int, default=5000,
|
|
||||||
help="Max bars of history (default 5000 — TD ceiling)")
|
|
||||||
parser.add_argument("--start", default=None,
|
|
||||||
help="Backtest window start (YYYY-MM-DD)")
|
|
||||||
parser.add_argument("--end", default=None,
|
|
||||||
help="Backtest window end (YYYY-MM-DD)")
|
|
||||||
parser.add_argument("--min-grade", default="B",
|
|
||||||
choices=["A+", "A", "B", "C", "D", "F"],
|
|
||||||
help="Minimum grade to open backtest trade (default B)")
|
|
||||||
parser.add_argument("--journal", action="store_true",
|
|
||||||
help="Print last 10 trades from backtest journal")
|
|
||||||
parser.add_argument("--journal-csv", default=None,
|
|
||||||
help="Write full trade journal to CSV path")
|
|
||||||
parser.add_argument("--regime-filter", action="store_true",
|
|
||||||
help="Gate signals on higher-TF ADX trend confirmation")
|
|
||||||
parser.add_argument("--regime-min-adx", type=float, default=30.0,
|
|
||||||
help="Min ADX for regime filter (default 30, requires --regime-filter)")
|
|
||||||
parser.add_argument("--session-filter", action="store_true",
|
|
||||||
help="Only trade during a UTC hour window (default 07-16, London + NY)")
|
|
||||||
parser.add_argument("--session-start", type=int, default=7,
|
|
||||||
help="Session start hour UTC, inclusive (default 7)")
|
|
||||||
parser.add_argument("--session-end", type=int, default=16,
|
|
||||||
help="Session end hour UTC, exclusive (default 16)")
|
|
||||||
parser.add_argument("--auto-session-filter", action="store_true",
|
|
||||||
help="Auto-toggle session filter when recent trades show adverse mean pnl%%")
|
|
||||||
parser.add_argument("--auto-session-lookback", type=int, default=10,
|
|
||||||
help="Lookback in closed trades for auto-session detection (default 10)")
|
|
||||||
parser.add_argument("--auto-session-threshold-pct", type=float, default=-0.3,
|
|
||||||
help="Mean pnl%% threshold below which auto-session activates (default -0.3)")
|
|
||||||
parser.add_argument("--ablate", action="store_true",
|
|
||||||
help="Run leave-one-out indicator ablation sweep (parallel)")
|
|
||||||
|
|
||||||
return parser.parse_args()
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
@@ -372,21 +280,6 @@ def build_config(args) -> PlatformConfig:
|
|||||||
args.timeframe, [Timeframe.M15, Timeframe.H4]
|
args.timeframe, [Timeframe.M15, Timeframe.H4]
|
||||||
)
|
)
|
||||||
|
|
||||||
# Backtest config
|
|
||||||
config.backtest.start_date = args.start
|
|
||||||
config.backtest.end_date = args.end
|
|
||||||
config.backtest.max_bars = args.bars
|
|
||||||
config.backtest.min_trade_grade = args.min_grade
|
|
||||||
config.backtest.optimize = args.optimize
|
|
||||||
config.backtest.regime_filter = args.regime_filter
|
|
||||||
config.backtest.regime_filter_min_adx = args.regime_min_adx
|
|
||||||
config.backtest.session_filter = args.session_filter
|
|
||||||
config.backtest.session_start_hour = args.session_start
|
|
||||||
config.backtest.session_end_hour = args.session_end
|
|
||||||
config.backtest.auto_session_filter = args.auto_session_filter
|
|
||||||
config.backtest.auto_session_lookback_trades = args.auto_session_lookback
|
|
||||||
config.backtest.auto_session_threshold_pct = args.auto_session_threshold_pct
|
|
||||||
|
|
||||||
return config
|
return config
|
||||||
|
|
||||||
|
|
||||||
@@ -406,148 +299,6 @@ def run_live(args, config: PlatformConfig):
|
|||||||
print(f"{Colors.CYAN}{'═' * 80}{Colors.RESET}\n")
|
print(f"{Colors.CYAN}{'═' * 80}{Colors.RESET}\n")
|
||||||
|
|
||||||
|
|
||||||
def run_backtest(args, config: PlatformConfig):
|
|
||||||
from backtest import WalkForwardBacktester
|
|
||||||
from metrics import trades_to_dataframe
|
|
||||||
|
|
||||||
print_header("USD/JPY WALK-FORWARD BACKTEST")
|
|
||||||
print(f"{Colors.GRAY} Loading data and replaying bars...{Colors.RESET}\n")
|
|
||||||
|
|
||||||
backtester = WalkForwardBacktester(config)
|
|
||||||
result = backtester.run()
|
|
||||||
|
|
||||||
if args.json:
|
|
||||||
out = {
|
|
||||||
"metrics": result.metrics,
|
|
||||||
"initial_balance": result.initial_balance,
|
|
||||||
"final_balance": result.final_balance,
|
|
||||||
"trades": [
|
|
||||||
{
|
|
||||||
"entry_time": str(t.entry_time),
|
|
||||||
"exit_time": str(t.exit_time),
|
|
||||||
"direction": t.direction,
|
|
||||||
"entry_price": t.entry_price,
|
|
||||||
"exit_price": t.exit_price,
|
|
||||||
"exit_reason": t.exit_reason,
|
|
||||||
"pnl_pips": t.pnl_pips,
|
|
||||||
"pnl_pct": t.pnl_pct,
|
|
||||||
"grade": t.grade,
|
|
||||||
}
|
|
||||||
for t in result.trades
|
|
||||||
],
|
|
||||||
}
|
|
||||||
print(json.dumps(out, indent=2))
|
|
||||||
else:
|
|
||||||
print_backtest_report(result, verbose_journal=args.journal)
|
|
||||||
|
|
||||||
if args.journal_csv and result.trades:
|
|
||||||
df = trades_to_dataframe(result.trades)
|
|
||||||
df.to_csv(args.journal_csv, index=False)
|
|
||||||
print(f"\n {Colors.GREEN}✓ Trade journal written to {args.journal_csv}{Colors.RESET}")
|
|
||||||
|
|
||||||
|
|
||||||
def run_optimize(args, config: PlatformConfig):
|
|
||||||
from backtest import Optimizer
|
|
||||||
|
|
||||||
print_header("USD/JPY BACKTEST PARAMETER OPTIMIZER")
|
|
||||||
grid = config.backtest.param_grid
|
|
||||||
n_combos = 1
|
|
||||||
for v in grid.values():
|
|
||||||
n_combos *= len(v)
|
|
||||||
print(f"{Colors.GRAY} Grid: {n_combos} combos × {config.backtest.parallel_workers} workers{Colors.RESET}\n")
|
|
||||||
|
|
||||||
optimizer = Optimizer(config)
|
|
||||||
results = optimizer.run_grid(grid)
|
|
||||||
|
|
||||||
print(f"\n {Colors.BOLD}Top 10 Parameter Combinations (by total return):{Colors.RESET}\n")
|
|
||||||
print(f" {'#':<3} {'Return%':>8} {'PF':>6} {'WinR':>6} {'Trades':>7} "
|
|
||||||
f"{'MaxDD%':>7} {'Sharpe':>7} Params")
|
|
||||||
print(f" {Colors.GRAY}{'─' * 90}{Colors.RESET}")
|
|
||||||
|
|
||||||
for i, r in enumerate(results[:10], start=1):
|
|
||||||
m = r.metrics
|
|
||||||
ret = m.get("total_return_pct", 0)
|
|
||||||
ret_color = Colors.GREEN if ret >= 0 else Colors.RED
|
|
||||||
params_str = ", ".join(f"{k}={v}" for k, v in r.params.items())
|
|
||||||
pf = m.get("profit_factor", 0)
|
|
||||||
pf_str = "inf" if pf == float("inf") else f"{pf:.2f}"
|
|
||||||
print(f" {i:<3} {ret_color}{ret:>+7.2f}%{Colors.RESET} {pf_str:>6} "
|
|
||||||
f"{m.get('win_rate', 0):>5.1f}% {m.get('total_trades', 0):>7} "
|
|
||||||
f"{m.get('max_drawdown_pct', 0):>+6.2f}% {m.get('sharpe', 0):>7.2f}"
|
|
||||||
f" {Colors.GRAY}{params_str}{Colors.RESET}")
|
|
||||||
|
|
||||||
print(f"\n {Colors.BOLD}Best combination details:{Colors.RESET}\n")
|
|
||||||
print_backtest_report(results[0], params=results[0].params, verbose_journal=False)
|
|
||||||
|
|
||||||
|
|
||||||
def run_ablation(args, config: PlatformConfig):
|
|
||||||
"""Leave-one-out ablation: baseline + each indicator disabled, in parallel."""
|
|
||||||
from backtest import Optimizer
|
|
||||||
from config import ALL_INDICATORS
|
|
||||||
|
|
||||||
print_header("USD/JPY INDICATOR ABLATION SWEEP")
|
|
||||||
|
|
||||||
grid = {"disable_indicator": [None] + list(ALL_INDICATORS)}
|
|
||||||
n_combos = len(grid["disable_indicator"])
|
|
||||||
print(f"{Colors.GRAY} Grid: {n_combos} runs (1 baseline + {n_combos-1} ablations) "
|
|
||||||
f"× {config.backtest.parallel_workers} workers{Colors.RESET}\n")
|
|
||||||
|
|
||||||
optimizer = Optimizer(config)
|
|
||||||
results = optimizer.run_grid(grid)
|
|
||||||
|
|
||||||
# Find baseline (disable_indicator=None) for delta computation
|
|
||||||
baseline = next(
|
|
||||||
(r for r in results if not r.params.get("disable_indicator")),
|
|
||||||
None,
|
|
||||||
)
|
|
||||||
base_ret = baseline.metrics.get("total_return_pct", 0.0) if baseline else 0.0
|
|
||||||
base_pf = baseline.metrics.get("profit_factor", 0.0) if baseline else 0.0
|
|
||||||
base_sharpe = baseline.metrics.get("sharpe", 0.0) if baseline else 0.0
|
|
||||||
|
|
||||||
# Sort: baseline first, then ablations by impact (largest drop = most useful indicator)
|
|
||||||
ablations = [r for r in results if r is not baseline]
|
|
||||||
ablations.sort(
|
|
||||||
key=lambda r: r.metrics.get("total_return_pct", 0.0) - base_ret
|
|
||||||
)
|
|
||||||
|
|
||||||
print(f" {Colors.BOLD}Indicator Ablation Report{Colors.RESET}")
|
|
||||||
print(f" {Colors.GRAY}{'─' * 88}{Colors.RESET}")
|
|
||||||
print(f" {'Disabled':<14} {'Return%':>9} {'ΔRet%':>8} {'PF':>6} {'ΔPF':>7} "
|
|
||||||
f"{'Sharpe':>7} {'ΔSh':>7} {'Trades':>7}")
|
|
||||||
print(f" {Colors.GRAY}{'─' * 88}{Colors.RESET}")
|
|
||||||
|
|
||||||
def fmt_row(label, m, deltas=True):
|
|
||||||
ret = m.get("total_return_pct", 0.0)
|
|
||||||
pf = m.get("profit_factor", 0.0)
|
|
||||||
sh = m.get("sharpe", 0.0)
|
|
||||||
tr = m.get("total_trades", 0)
|
|
||||||
ret_color = Colors.GREEN if ret >= 0 else Colors.RED
|
|
||||||
pf_str = "inf" if pf == float("inf") else f"{pf:.2f}"
|
|
||||||
if deltas:
|
|
||||||
d_ret = ret - base_ret
|
|
||||||
d_pf = pf - base_pf if pf != float("inf") else 0.0
|
|
||||||
d_sh = sh - base_sharpe
|
|
||||||
d_ret_color = Colors.RED if d_ret < 0 else Colors.GREEN
|
|
||||||
print(f" {label:<14} {ret_color}{ret:>+8.2f}%{Colors.RESET} "
|
|
||||||
f"{d_ret_color}{d_ret:>+7.2f}%{Colors.RESET} "
|
|
||||||
f"{pf_str:>6} {d_pf:>+7.2f} "
|
|
||||||
f"{sh:>7.2f} {d_sh:>+7.2f} {tr:>7}")
|
|
||||||
else:
|
|
||||||
print(f" {Colors.BOLD}{label:<14}{Colors.RESET} "
|
|
||||||
f"{ret_color}{ret:>+8.2f}%{Colors.RESET} {'':>8} "
|
|
||||||
f"{pf_str:>6} {'':>7} {sh:>7.2f} {'':>7} {tr:>7}")
|
|
||||||
|
|
||||||
if baseline:
|
|
||||||
fmt_row("baseline", baseline.metrics, deltas=False)
|
|
||||||
print(f" {Colors.GRAY}{'─' * 88}{Colors.RESET}")
|
|
||||||
for r in ablations:
|
|
||||||
fmt_row(str(r.params.get("disable_indicator")), r.metrics)
|
|
||||||
|
|
||||||
print(f"\n {Colors.GRAY}Indicators ranked by importance — most negative ΔRet means "
|
|
||||||
f"removing it hurt most.{Colors.RESET}")
|
|
||||||
print(f" {Colors.GRAY}Positive ΔRet means the indicator is *hurting* the system.{Colors.RESET}\n")
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
args = parse_args()
|
args = parse_args()
|
||||||
|
|
||||||
@@ -559,15 +310,7 @@ def main():
|
|||||||
)
|
)
|
||||||
|
|
||||||
config = build_config(args)
|
config = build_config(args)
|
||||||
|
run_live(args, config)
|
||||||
if args.ablate:
|
|
||||||
run_ablation(args, config)
|
|
||||||
elif args.optimize:
|
|
||||||
run_optimize(args, config)
|
|
||||||
elif args.backtest:
|
|
||||||
run_backtest(args, config)
|
|
||||||
else:
|
|
||||||
run_live(args, config)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
-100001
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
-100001
File diff suppressed because it is too large
Load Diff
-169
@@ -1,169 +0,0 @@
|
|||||||
"""
|
|
||||||
ForexBot2 — Backtest Metrics
|
|
||||||
==============================
|
|
||||||
Pure functions over Trade records & equity curves.
|
|
||||||
"""
|
|
||||||
|
|
||||||
from dataclasses import dataclass, field, asdict
|
|
||||||
from typing import Dict, List, Optional
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class Trade:
|
|
||||||
"""A single closed trade."""
|
|
||||||
entry_time: pd.Timestamp
|
|
||||||
exit_time: pd.Timestamp
|
|
||||||
direction: str # "BUY" / "SELL"
|
|
||||||
entry_price: float
|
|
||||||
exit_price: float
|
|
||||||
stop_loss: float
|
|
||||||
take_profit: float
|
|
||||||
exit_reason: str # "tp" / "sl" / "end_of_data"
|
|
||||||
risk_pips: float
|
|
||||||
pnl_pips: float
|
|
||||||
pnl_pct: float # P&L as % of equity at trade open
|
|
||||||
pnl_currency: float # P&L in account currency (units = balance)
|
|
||||||
duration_bars: int
|
|
||||||
grade: str # signal grade at entry
|
|
||||||
score_at_entry: float
|
|
||||||
confidence_at_entry: float
|
|
||||||
# Diagnostic fields captured at entry — used to mine for loss-pattern
|
|
||||||
# filters when a window underperforms.
|
|
||||||
entry_hour: int = -1 # UTC hour 0-23
|
|
||||||
entry_dow: int = -1 # 0=Mon, 6=Sun
|
|
||||||
adx_at_entry: float = 0.0
|
|
||||||
atr_pct_at_entry: float = 0.0
|
|
||||||
market_regime: str = "" # trending_up / trending_down / ranging
|
|
||||||
volatility_regime: str = "" # low / medium / high
|
|
||||||
mtf_agreement: bool = False
|
|
||||||
m15_score: float = 0.0
|
|
||||||
h1_score: float = 0.0
|
|
||||||
h4_score: float = 0.0
|
|
||||||
rsi_at_entry: float = 50.0
|
|
||||||
ema_distance_pct: float = 0.0 # (price - ema_trend) / ema_trend * 100
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
|
||||||
class BacktestResult:
|
|
||||||
"""Complete backtest output."""
|
|
||||||
trades: List[Trade] = field(default_factory=list)
|
|
||||||
equity_curve: pd.Series = field(default_factory=lambda: pd.Series(dtype=float))
|
|
||||||
initial_balance: float = 0.0
|
|
||||||
final_balance: float = 0.0
|
|
||||||
metrics: Dict[str, float] = field(default_factory=dict)
|
|
||||||
params: Dict[str, float] = field(default_factory=dict)
|
|
||||||
|
|
||||||
|
|
||||||
def compute_metrics(
|
|
||||||
trades: List[Trade],
|
|
||||||
equity_curve: pd.Series,
|
|
||||||
initial_balance: float,
|
|
||||||
elapsed_days: Optional[float] = None,
|
|
||||||
) -> Dict[str, float]:
|
|
||||||
"""All summary metrics from a list of closed trades + equity curve."""
|
|
||||||
if not trades:
|
|
||||||
return {
|
|
||||||
"total_trades": 0,
|
|
||||||
"win_rate": 0.0,
|
|
||||||
"profit_factor": 0.0,
|
|
||||||
"expectancy_per_trade": 0.0,
|
|
||||||
"expectancy_pips": 0.0,
|
|
||||||
"total_return_pct": 0.0,
|
|
||||||
"cagr": 0.0,
|
|
||||||
"max_drawdown_pct": 0.0,
|
|
||||||
"sharpe": 0.0,
|
|
||||||
"sortino": 0.0,
|
|
||||||
"avg_trade_duration_bars": 0.0,
|
|
||||||
"longest_winning_streak": 0,
|
|
||||||
"longest_losing_streak": 0,
|
|
||||||
"final_balance": initial_balance,
|
|
||||||
}
|
|
||||||
|
|
||||||
pnl_currency = np.array([t.pnl_currency for t in trades])
|
|
||||||
pnl_pips = np.array([t.pnl_pips for t in trades])
|
|
||||||
|
|
||||||
wins = pnl_currency > 0
|
|
||||||
losses = pnl_currency < 0
|
|
||||||
|
|
||||||
total_wins = pnl_currency[wins].sum() if wins.any() else 0.0
|
|
||||||
total_losses = -pnl_currency[losses].sum() if losses.any() else 0.0
|
|
||||||
|
|
||||||
win_rate = wins.sum() / len(trades) * 100
|
|
||||||
profit_factor = (total_wins / total_losses) if total_losses > 0 else float("inf")
|
|
||||||
expectancy_per_trade = pnl_currency.mean()
|
|
||||||
expectancy_pips = pnl_pips.mean()
|
|
||||||
|
|
||||||
final_balance = float(equity_curve.iloc[-1]) if len(equity_curve) else initial_balance
|
|
||||||
total_return_pct = (final_balance / initial_balance - 1) * 100
|
|
||||||
|
|
||||||
# CAGR
|
|
||||||
if elapsed_days and elapsed_days > 0 and final_balance > 0:
|
|
||||||
years = elapsed_days / 365.25
|
|
||||||
cagr = ((final_balance / initial_balance) ** (1 / years) - 1) * 100 if years > 0 else 0.0
|
|
||||||
else:
|
|
||||||
cagr = 0.0
|
|
||||||
|
|
||||||
# Max drawdown
|
|
||||||
if len(equity_curve):
|
|
||||||
running_max = equity_curve.cummax()
|
|
||||||
drawdown = (equity_curve - running_max) / running_max
|
|
||||||
max_dd = float(drawdown.min()) * 100
|
|
||||||
else:
|
|
||||||
max_dd = 0.0
|
|
||||||
|
|
||||||
# Sharpe / Sortino — daily returns from equity curve
|
|
||||||
sharpe, sortino = 0.0, 0.0
|
|
||||||
if len(equity_curve) > 2:
|
|
||||||
daily = equity_curve.resample("1D").last().ffill().pct_change().dropna()
|
|
||||||
if len(daily) > 1 and daily.std() > 0:
|
|
||||||
sharpe = float(daily.mean() / daily.std() * np.sqrt(252))
|
|
||||||
downside = daily[daily < 0]
|
|
||||||
if len(downside) > 1 and downside.std() > 0:
|
|
||||||
sortino = float(daily.mean() / downside.std() * np.sqrt(252))
|
|
||||||
|
|
||||||
# Streaks
|
|
||||||
win_loss_seq = [1 if t.pnl_currency > 0 else (-1 if t.pnl_currency < 0 else 0) for t in trades]
|
|
||||||
longest_win = _longest_streak(win_loss_seq, 1)
|
|
||||||
longest_loss = _longest_streak(win_loss_seq, -1)
|
|
||||||
|
|
||||||
avg_duration = float(np.mean([t.duration_bars for t in trades]))
|
|
||||||
|
|
||||||
return {
|
|
||||||
"total_trades": len(trades),
|
|
||||||
"wins": int(wins.sum()),
|
|
||||||
"losses": int(losses.sum()),
|
|
||||||
"win_rate": float(win_rate),
|
|
||||||
"profit_factor": float(profit_factor),
|
|
||||||
"expectancy_per_trade": float(expectancy_per_trade),
|
|
||||||
"expectancy_pips": float(expectancy_pips),
|
|
||||||
"total_return_pct": float(total_return_pct),
|
|
||||||
"cagr": float(cagr),
|
|
||||||
"max_drawdown_pct": float(max_dd),
|
|
||||||
"sharpe": float(sharpe),
|
|
||||||
"sortino": float(sortino),
|
|
||||||
"avg_trade_duration_bars": avg_duration,
|
|
||||||
"longest_winning_streak": longest_win,
|
|
||||||
"longest_losing_streak": longest_loss,
|
|
||||||
"final_balance": final_balance,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def _longest_streak(seq: List[int], target: int) -> int:
|
|
||||||
best = current = 0
|
|
||||||
for x in seq:
|
|
||||||
if x == target:
|
|
||||||
current += 1
|
|
||||||
best = max(best, current)
|
|
||||||
else:
|
|
||||||
current = 0
|
|
||||||
return best
|
|
||||||
|
|
||||||
|
|
||||||
def trades_to_dataframe(trades: List[Trade]) -> pd.DataFrame:
|
|
||||||
"""Convert trade journal to a DataFrame for CSV/JSON export."""
|
|
||||||
if not trades:
|
|
||||||
return pd.DataFrame()
|
|
||||||
return pd.DataFrame([asdict(t) for t in trades])
|
|
||||||
+1
-2
@@ -1,7 +1,6 @@
|
|||||||
numpy>=1.24
|
numpy>=1.24
|
||||||
pandas>=2.0
|
pandas>=2.0
|
||||||
requests>=2.28
|
requests>=2.28
|
||||||
pyarrow>=14.0
|
|
||||||
tqdm>=4.66
|
|
||||||
twelvedata>=1.2.0
|
twelvedata>=1.2.0
|
||||||
python-telegram-bot>=20.0
|
python-telegram-bot>=20.0
|
||||||
|
websockets>=12.0
|
||||||
|
|||||||
@@ -2,9 +2,7 @@
|
|||||||
ForexBot2 — Signal Aggregator
|
ForexBot2 — Signal Aggregator
|
||||||
==============================
|
==============================
|
||||||
Combines multi-timeframe technical analysis with optional news context
|
Combines multi-timeframe technical analysis with optional news context
|
||||||
into a graded actionable signal. Same code path is used for live mode
|
into a graded actionable signal.
|
||||||
and walk-forward backtesting; the only difference is whether the input
|
|
||||||
OHLCV is fetched live or sliced from a pre-loaded historical buffer.
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import logging
|
import logging
|
||||||
@@ -79,10 +77,7 @@ class SignalReport:
|
|||||||
|
|
||||||
|
|
||||||
class SignalAggregator:
|
class SignalAggregator:
|
||||||
"""
|
"""Master aggregator for live signal analysis."""
|
||||||
Master aggregator. Designed so the backtester can drive it bar-by-bar
|
|
||||||
via `analyze_from_dataframes` without re-fetching from the API.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, config: PlatformConfig):
|
def __init__(self, config: PlatformConfig):
|
||||||
self.config = config
|
self.config = config
|
||||||
|
|||||||
@@ -0,0 +1,55 @@
|
|||||||
|
"""Live tick stream from Twelve Data WebSocket API."""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import asyncio
|
||||||
|
import websockets
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
|
||||||
|
|
||||||
|
API_KEY = "f563b22ffdfd4924955decbe1a8c8701"
|
||||||
|
WS_URL = f"wss://ws.twelvedata.com/v1/quotes/price?apikey={API_KEY}"
|
||||||
|
SYMBOLS = ["EUR/USD", "GBP/USD", "USD/JPY"]
|
||||||
|
|
||||||
|
|
||||||
|
async def stream_ticks():
|
||||||
|
async with websockets.connect(WS_URL) as ws:
|
||||||
|
subscribe = {
|
||||||
|
"action": "subscribe",
|
||||||
|
"params": {
|
||||||
|
"symbols": ",".join(SYMBOLS),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
await ws.send(json.dumps(subscribe))
|
||||||
|
print(f"Subscribed to: {', '.join(SYMBOLS)}")
|
||||||
|
print("-" * 60)
|
||||||
|
|
||||||
|
async for raw in ws:
|
||||||
|
msg = json.loads(raw)
|
||||||
|
|
||||||
|
if msg.get("event") == "subscribe-status":
|
||||||
|
print(f"Status: {msg.get('status')} — {msg.get('message', '')}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
if msg.get("event") == "heartbeat":
|
||||||
|
continue
|
||||||
|
|
||||||
|
if msg.get("event") == "price":
|
||||||
|
ts = datetime.fromtimestamp(msg["timestamp"], tz=timezone.utc)
|
||||||
|
symbol = msg["symbol"]
|
||||||
|
price = msg["price"]
|
||||||
|
print(f"{ts:%H:%M:%S} | {symbol:<8} | {price}")
|
||||||
|
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
print(f"Connecting to Twelve Data WebSocket...")
|
||||||
|
print(f"API key: ...{API_KEY[-6:]}")
|
||||||
|
try:
|
||||||
|
await stream_ticks()
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print("\nStopped.")
|
||||||
|
except websockets.exceptions.ConnectionClosed as e:
|
||||||
|
print(f"\nConnection closed: {e}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
Reference in New Issue
Block a user