# ForexBot2

XAU/USD signal intelligence platform. Analyzes gold across multiple timeframes using 9 technical indicators with regime-adaptive weights, news sentiment, and economic calendar data. Delivers signals via Telegram with full trade setups (entry, SL, TP).

## Architecture

```
Twelve Data REST API + WebSocket
        |
  data_provider.py          (fetch, cache, validate OHLCV; rate-limited 55 calls/min)
        |
    engine.py                (9 indicators per timeframe → composite score)
        |
  signal_aggregator.py       (MTF agreement + news + final signal + trade setup)
        |
  +-----+--------+
  |              |
telegram_bot.py  main.py
(Telegram alerts, (CLI single-pair
 auto-scanner,    analysis)
 WS monitor)
```

## Core Files

| File | Purpose |
|------|---------|
| `config.py` | All configuration dataclasses, XAU/USD pair definition, strategy config |
| `data_provider.py` | Twelve Data SDK wrapper with in-memory cache and rate limiting |
| `engine.py` | `TechnicalEngine` — computes EMA, RSI, BB, ADX, ATR, MACD, ROC, Keltner, divergences, candlestick patterns, swing S/R |
| `signal_aggregator.py` | `SignalAggregator` — MTF analysis, news integration, diminishing penalty cap, trade setup generation |
| `news_engine.py` | Economic calendar (Forex Factory JSON) + RSS sentiment (keyword-based) |
| `telegram_bot.py` | Async Telegram bot with auto-scanner (M5/M15/H1/H4), WebSocket price monitor |
| `main.py` | CLI entry point for XAU/USD analysis |
| `test_all.py` | Comprehensive QA suite (~137 tests, no live API needed) |

## Key Concepts

**Signal Pipeline:** OHLCV → indicators → regime classification → regime-weighted composite score → MTF agreement check → news/event adjustment → diminishing penalty cap → final signal (STRONG_BUY/BUY/NEUTRAL/SELL/STRONG_SELL) → quality grade (A+/A/B/C/D/F) → trade setup if grade C+.

**Regime-Adaptive Weights:** Market classified as `trending_up`, `trending_down`, `ranging`, or `transitioning` via ADX + EMA + ADX slope. Gold-specific weights per regime:
- **Trending:** MACD (2.0), EMA (2.0), Keltner (1.5), ROC (1.5), Divergence (1.0)
- **Ranging:** Divergence (2.5), Keltner (2.0), RSI (1.5), BB (1.5), MACD (1.5)
- **Transitioning:** Keltner (2.5), MACD (2.5), ROC (2.0), Divergence (2.0), EMA (1.5)

ROC is directionally redundant with MACD/Keltner/EMA (0.8–0.94 correlation) but its weight is load-bearing: composite thresholds are calibrated with it — removing it backtested 13R worse. Don't zero it without retuning thresholds.

**RSI (regime-aware, default):** In trends RSI buys pullback zones instead of fading strength (classic mode backtested worse for gold). In `transitioning` only extremes (≥70/≤30) signal.

**Divergence:** 40-bar lookback; price and indicator swings must align within ±2 bars to count as one divergence observation.

**Transitioning Regime:** ADX < 25 but ADX slope > 3.0 = trend emerging. Catches trend birth 5-10 bars before ADX crosses 25. Boosts quality grade for early entries.

**Diminishing Penalty Cap:** Penalties sorted by magnitude, applied with exponential decay (1st at 100%, 2nd at 50%, 3rd at 25%). Hard cap at 30% total. Replaces old additive stacking that could gut 30-50% confidence.

**Indicators (9 scored):** EMA Trend, RSI, Bollinger Bands, MACD, ADX, Keltner Channels (TTM Squeeze), ROC (Rate of Change), Divergence, Candlestick Patterns.

**Trade Setup:** SL = nearest swing low/high ± 1×ATR buffer (min 1.5×ATR). TP = nearest swing level if RR≥1.2, else calculated from ATR. Minimum RR 0.5:1.

## How to Run

```bash
# CLI (XAU/USD analysis)
python main.py                    # H1 default
python main.py --timeframe M15 --detail --json

# Telegram bot (auto-scanner)
TELEGRAM_TOKEN=<token> TELEGRAM_CHAT_ID=<id> python telegram_bot.py

# Tests
python test_all.py

# Backtest
python backtest/gpu_backtest.py --pair XAUUSD --days 365
```

## Environment Variables

| Variable | Purpose | Default |
|----------|---------|---------|
| `TWELVE_DATA_API_KEY` | Market data API key | Hardcoded fallback in config |
| `TELEGRAM_TOKEN` | Telegram bot token | Hardcoded fallback |
| `TELEGRAM_CHAT_ID` | Auto-scan chat ID (enables boot auto-scan) | Hardcoded fallback |

## Dependencies

`numpy`, `pandas`, `requests`, `twelvedata`, `python-telegram-bot`, `websockets` — see `requirements.txt`.

## Data Provider

- **Source:** Twelve Data REST API via `twelvedata` SDK
- **Rate limit:** 55 credits/min (Grow plan), enforced by sliding-window throttle
- **Cache:** In-memory dict, TTL = candle duration of the timeframe
- **Validation:** Drops NaN rows, forward-fills gaps (limit 3), fixes OHLC inconsistencies, removes outliers (>10σ), strips future timestamps, deduplicates
- **WebSocket:** XAU/USD only

## Telegram Bot

- **Auto-scan:** M5, M15, H1, H4 at candle boundaries + 10s buffer (all timeframes alert)
- **Manual scan:** Buttons for M15/H1/H4 individual scans + "Scan All TFs" multi-timeframe summary
- **WebSocket:** Streams live ticks for XAU/USD; triggers immediate M15 scan on key level breach (swing S/R, BB, KC, EMA200) with 5-min cooldown
- **Commands:** `/start`, `/scan`, `/autoscan`, `/status`, `/cache`, `/clearcache`, `/help`

## Backtesting

The `backtest/` directory contains `gpu_backtest.py` — a GPU-accelerated backtester (CuPy with NumPy fallback) that replays historical OHLCV through the real SignalAggregator. Pre-computes all indicator series including ROC, Keltner, ADX slope for 100x+ speedup. Uses parquet caching for data persistence.

Fill realism: charges a round-trip spread per trade (`--spread`, default $0.30 for XAU), fills gap-through SL/TP at the bar open, and resolves trades against the RAW price path (the parquet cache stores raw bars; validation/cleaning runs on load — signals see cleaned data, trade resolution sees real spikes). Entry is at the next bar's open after the signal bar. Weekend/session filters evaluate bar-close (decision) time to match the live bot's wall clock.

GPU note: CuPy needs the NVIDIA runtime pip wheels (`nvidia-cuda-runtime-cu12`, `nvidia-cuda-nvrtc-cu12`, `nvidia-curand-cu12`, `nvidia-nvjitlink-cu12`) or it silently falls back to NumPy — check the "Backend:" line in the run log. The bar-replay loop itself is single-threaded Python either way (~340 bars/s); GPU only accelerates indicator precompute and SL/TP scanning.

## Code Conventions

- Python 3.12, type hints on public methods
- Dataclasses for all config and output structures
- `SignalStrength` enum (-2 to +2) is the universal signal currency
- Indicators stored as `Dict[str, IndicatorSignal]` in `TechnicalAnalysis`
- Key levels stored as `Dict[str, float]` — prefixed `swing_high_`, `swing_low_`, `ema_`, `bb_`, `kc_`, `atr`
- Private indicator data attached as `result._rsi_series`, `result._macd_histogram`, `result._adx_slope`, `result._kc_squeeze` (underscore prefix, skipped by composite scorer)
- Blocking signal computation runs in `ThreadPoolExecutor(max_workers=4)` to avoid blocking the async Telegram event loop
- Shared `ForexDataProvider` instance across all scans for cache reuse