# ForexBot2 — Engine Logic & Design Reference

> USD/JPY signal intelligence platform.  
> Last updated: 2026-05-08

---

## Table of Contents

1. [Architecture Overview](#1-architecture-overview)
2. [Data Pipeline](#2-data-pipeline)
3. [Indicator Engine (`engine.py`)](#3-indicator-engine)
4. [Signal Aggregator (`signal_aggregator.py`)](#4-signal-aggregator)
5. [Composite Scoring Formula](#5-composite-scoring-formula)
6. [Indicator Weights — Optimised](#6-indicator-weights--optimised)
7. [Market Regime Classification](#7-market-regime-classification)
8. [EMA200 Distance Gate](#8-ema200-distance-gate)
9. [Multi-Timeframe Alignment](#9-multi-timeframe-alignment)
10. [Signal Grade System](#10-signal-grade-system)
11. [GPU Backtester (`backtest_gpu.py`)](#11-gpu-backtester)
12. [Backtesting Results Summary](#12-backtesting-results-summary)
13. [Filter Catalogue](#13-filter-catalogue)
14. [Configuration Reference](#14-configuration-reference)

---

## 1. Architecture Overview

```
market_data/ (local CSV)
      │
      ▼
ForexDataProvider          ← also pulls live via Twelve Data SDK
      │
      ▼
TechnicalEngine            ← computes all indicators bar-by-bar
      │
      ▼
SignalAggregator           ← MTF blending, EMA200 gate, grading
      │
      ├──▶ Live output (main.py)
      └──▶ GPUBacktester   ← vectorised O(N) replay over full history
```

**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.

---

## 2. Data Pipeline

### Live mode
`ForexDataProvider.fetch_ohlcv()` calls the Twelve Data SDK with in-memory caching (5-min TTL). Fetches primary TF + two confirmation TFs + optional regime TF.

### Backtest mode
`ForexDataProvider.load_local_csv()` reads MetaTrader tab-delimited exports from `market_data/`:

| File | TF | Rows | Date Range |
|------|----|------|------------|
| USDJPY_M15.csv | M15 | 100,000 | 2022-05-04 → 2026-05-08 |
| USDJPY_H1.csv | H1 | 100,000 | 2010-04-23 → 2026-05-08 |
| USDJPY_H4.csv | H4 | 25,855 | 2010-04-22 → 2026-05-08 |
| USDJPY_D1.csv | D1 | 5,020 | 2010-04-22 → 2026-05-08 |

CSV format: tab-delimited, columns `Time | Open | High | Low | Close | Volume | Spread`. Spread column is dropped on load. Timestamps are UTC.

**Data validation** (applied to both sources):
- Drop NaN OHLC rows; forward-fill up to 3 bars
- Drop future timestamps (>5 min ahead of UTC now)
- Fix OHLC inconsistencies (High < Low etc.)
- Remove outlier bars where `|return| > 10 × rolling_std`

---

## 3. Indicator Engine

`engine.py → TechnicalEngine.analyze(df, symbol, timeframe)`

All indicators are computed on a single OHLCV DataFrame. Each returns an `IndicatorSignal` with:
- `value` — raw numeric reading
- `signal` — `SignalStrength` enum: STRONG_BUY(+2) / BUY(+1) / NEUTRAL(0) / SELL(-1) / STRONG_SELL(-2)
- `confidence` — 0–100%
- `weight` — contribution to composite score (0 = excluded)

### Indicators computed

| Name | Key | Wilder smoothing? | Notes |
|------|-----|:-----------------:|-------|
| EMA Alignment | `ema_trend` | No | 4-EMA stack: 9/21/50/200; normalised to -1..+1 |
| RSI | `rsi` | Yes (`alpha=1/14`) | Overbought/oversold + momentum zones |
| MACD | `macd` | No | 12/26/9; crossover = STRONG signal |
| Bollinger Bands | `bollinger` | No | 20-period; position within band + squeeze detect |
| Stochastic | `stochastic` | No | 14/3; K+D both OB/OS required for signal |
| ADX | `adx` | Yes | DI+/DI- direction + ADX strength gate (≥25) |
| ATR | `atr` | Yes | Context only — sets SL/TP distance; weight=0 |
| Ichimoku | `ichimoku` | No | Price vs cloud (+2/-2) + TK cross (+1/-1) + cloud colour (+1/-1) |
| Divergence | `divergence` | — | RSI/MACD price vs indicator swing divergence; weight=1.5 |
| Candlestick | `candlestick` | — | **Disabled** (ablation: net -3.4%) |
| Pivot Points | `pivot` | — | **Disabled** (ablation: net -9.5%) |
| Volume | `volume` | — | Context filter; weight=0 |

**Disabled indicators** are controlled by `TechnicalConfig.disabled_indicators` (default: `["pivot", "candlestick"]`).

---

## 4. Signal Aggregator

`signal_aggregator.py → SignalAggregator.analyze()`

Orchestrates three timeframes (primary + 2 confirmation), blends their scores, applies gates, and emits a final graded signal.

### Flow

```
H1 (primary)  ──┐
M15 (confirm) ──┼──▶ per-TF score ──▶ MTF blend ──▶ EMA200 gate ──▶ grade ──▶ output
H4 (confirm)  ──┘
```

### MTF alignment (no lookahead)

Lower-frequency bars are aligned to the primary TF index by forward-filling at **close times**:

```
secondary close time = secondary bar open + secondary TF minutes
primary   close time = primary bar open   + primary TF minutes

For each primary bar T: use the most recent secondary close ≤ T_close
```

This ensures no future information leaks into the signal.

---

## 5. Composite Scoring Formula

Mirrors `engine._compute_composite` exactly:

```
composite_score = clip(
    (Σ  signal_i × weight_i × regime_mult_i × (confidence_i / 100))
    ──────────────────────────────────────────────────────────────────  × 50,
    (Σ  weight_i × regime_mult_i)
    -100, +100
)

avg_confidence = Σ(confidence_i × weight_i) / Σ weight_i
```

- `signal_i` ∈ {-2, -1, 0, +1, +2}
- `weight_i` — per-indicator base weight (see §6)
- `regime_mult_i` — boosted/dampened by market regime (see §7)
- Indicators with `weight == 0` are excluded entirely

---

## 6. Indicator Weights — Optimised

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.

| Indicator | Weight | Change from original | Reason |
|-----------|-------:|---------------------|--------|
| `ema_trend` | **3.6** | was 1.2 (×3) | Strongest directional signal on trending pair |
| `macd` | **3.3** | was 1.1 (×3) | Most impactful single indicator; ablation = worst result |
| `stochastic` | **1.6** | was 0.8 (×2) | Clean timing signal; 40% WR at ×2 |
| `adx` | **0.5** | was 1.0 (÷2) | Partially redundant with EMA stack |
| `ichimoku` | **0.55** | was 1.1 (÷2) | Lags too much at H4; hurts on ranging periods |
| `rsi` | **0.0** | was 0.3 (ablated) | Net drag across all test windows |
| `bollinger` | **0.0** | was 0.9 (ablated) | Counter-trend noise on trending pair |
| `divergence` | 1.5 | unchanged | Strong reversal signal; not in sweep scope |
| `atr` | 0.0 | unchanged | Context only; sets SL/TP |

> **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.

### Regime weight multipliers

Applied on top of base weights:

| Indicator | Trending mult | Ranging mult |
|-----------|:-------------:|:------------:|
| ema_trend | ×1.5 | ×0.6 |
| adx | ×1.3 | ×0.5 |
| macd | ×1.2 | ×1.0 |
| ichimoku | ×1.3 | ×0.2 |
| rsi | ×0.7 | ×1.4 |
| bollinger | ×0.6 | ×1.4 |
| stochastic | ×1.0 | ×1.3 |

In trending regimes: EMA trend, MACD, and Ichimoku are boosted further.  
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.

---

## 7. Market Regime Classification

`engine._classify_regime()` — runs after all indicators are computed:

```
if ADX ≥ 25:
    if EMA signal is BUY or STRONG_BUY → trending_up
    if EMA signal is SELL or STRONG_SELL → trending_down
    else → trending
else:
    → ranging
```

Regime feeds directly into the weight multipliers applied in `_compute_composite`.

---

## 8. EMA200 Distance Gate

**Strongest single filter discovered** — implemented in `signal_aggregator.py`.

```
ema_dist = |close - EMA200| / EMA200 × 100  (percent)

if ema_dist < 0.5% or ema_dist > 1.5%:
    confidence *= 0.55          ← damped
else:                           ← sweet spot
    no change
```

**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.

**Impact measured:** Lifted cumulative return +13.88% → +35.50% in the 2024-trend ablation window.

---

## 9. Multi-Timeframe Alignment

Primary TF: **H1** (default). Confirmation TFs: **M15** and **H4**.

### MTF blending (when 2 of 3 TFs agree)

```
mtf_agree = (bull_count ≥ 2) OR (bear_count ≥ 2)

if mtf_agree:
    score = 0.6 × primary_score + 0.4 × mean(all_scores)
    confidence = min(confidence + 15, 95)
else:
    confidence = confidence × 0.7       ← penalised for disagreement
```

### MTF agreement bonus in grade assignment

`mtf_agree=True` is required for grades A and A+.

---

## 10. Signal Grade System

| Grade | Conditions |
|-------|-----------|
| **A+** | `|score| ≥ 50` AND `conf ≥ 75%` AND `mtf_agree` |
| **A** | `|score| ≥ 35` AND `conf ≥ 65%` AND `mtf_agree` |
| **B** | `|score| ≥ 25` AND `conf ≥ 55%` |
| **C** | `|score| ≥ 15` AND `conf ≥ 45%` |
| **D** | `|score| ≥ 10` |
| **F** | below all thresholds |

Default minimum grade for trade entry: **B** (`BacktestConfig.min_trade_grade`).

### Signal integer thresholds (for trade direction)

| Signal | Condition |
|--------|-----------|
| STRONG_BUY (+2) | score ≥ +40 AND conf ≥ 65% |
| BUY (+1) | score ≥ +15 AND conf ≥ 50% |
| STRONG_SELL (-2) | score ≤ -40 AND conf ≥ 65% |
| SELL (-1) | score ≤ -15 AND conf ≥ 50% |
| NEUTRAL | otherwise |

---

## 11. GPU Backtester

`backtest_gpu.py` — completely separate from `backtest.py`.

### Why O(N) instead of O(N²)

`backtest.py` slices the DataFrame and recomputes all indicators fresh each bar → N × O(N) = **O(N²)**.  
`backtest_gpu.py` computes every indicator series end-to-end once, then reads bar T's value in O(1) → **O(N) total**.

On 100,000 H1 bars: ~4 seconds vs >10 minutes.

### Two-phase architecture

```
Phase 1 — prepare_arrays(dfs)
    VectorIndicators.compute_all()     ← pandas ewm/rolling, C-optimised
    Align MTF signals to primary TF    ← forward-fill at close times
    Compute regime ADX series          ← if regime filter enabled
    Returns reusable bundle dict

Phase 2 — score_and_run(bundle, weights, disabled)
    compute_bar_scores()               ← CuPy GPU or NumPy CPU
    compute_final_scores()             ← MTF blend + EMA200 gate (vectorised)
    compute_grades()                   ← vectorised grade assignment
    run_state_machine()                ← sequential position management (O(N))
```

`prepare_arrays()` is called once; `score_and_run()` can be called many times with different weights — this is what the `--optimize` sweep uses.

### Optimization sweep (`--optimize`)

36 combinations tested per run:
- **1** baseline (default weights)
- **7** ablation runs (each indicator disabled one at a time)
- **28** weight multiplier runs (each indicator at ×0.5, ×1.5, ×2.0, ×3.0)

Indicator arrays computed once → all 36 replays run in seconds.

### Trade execution model

```
Entry: next bar Open ± (spread/2 + slippage)   [default: 0.5 pip slippage]
SL:    entry ± ATR × atr_sl_multiplier          [default: 1.5×]
TP:    entry ± SL_dist × rr_ratio               [default: 2.0× = 1:2 R:R]
Risk:  1% of current equity per trade
```

If SL and TP both touch within the same bar: SL assumed to hit first (`sl_first_on_tie=True`).

### CLI flags

```
python backtest_gpu.py --tf H4 --local               # local CSV, H4
python backtest_gpu.py --tf H1 --local --start 2022-01-01
python backtest_gpu.py --tf H4 --local --optimize    # 36-combo weight sweep
python backtest_gpu.py --regime-filter --regime-tf D1
python backtest_gpu.py --session-filter              # UTC 07-16 only
python backtest_gpu.py --journal --journal-csv out.csv
```

---

## 12. Backtesting Results Summary

### H4 — local CSV 2022–2026 (optimised weights)

| Metric | Value |
|--------|-------|
| Trades | 160 |
| Win Rate | 38.1% |
| Profit Factor | 1.19 |
| Total Return | +21.30% |
| Max Drawdown | -8.52% |
| Sharpe | 0.47 |
| 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"
```