ForexBot2/CLAUDE.md

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), Divergence (2.0), Keltner (1.5), ROC (1.5)
• 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)

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

# 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.

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