harwinder
98daf71a52
Signal daemon (signal_web.py): candle-boundary scanner mirroring telegram_bot.py, publishes serialized SignalReport JSON to Redis. Web API (web/app.py): thin FastAPI reader behind Apache, serves /api/signals REST + /ws WebSocket from Redis pub/sub. Frontend (web/static/index.html): 3-pair grid, color-coded signals, indicator breakdown per timeframe, trade setup display. Also: XAU-specific penalty scoping in signal_aggregator (softer confidence penalties for gold, original strict values for forex), pair-aware session filter routing XAU to its own blocked hours. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
5.6 KiB
ForexBot2
Automated forex signal intelligence platform. Analyzes 4 currency pairs (EUR/USD, GBP/USD, USD/JPY, XAU/USD) across multiple timeframes using technical indicators, 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 (6 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,
live dashboard)
Core Files
| File | Purpose |
|---|---|
config.py |
All configuration dataclasses, pair definitions, strategy configs |
data_provider.py |
Twelve Data SDK wrapper with in-memory cache and rate limiting |
engine.py |
TechnicalEngine — computes EMA, RSI, BB, ADX, ATR, MACD, divergences, candlestick patterns, swing S/R |
signal_aggregator.py |
SignalAggregator — MTF analysis, news integration, final signal scoring, 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, live dashboard |
main.py |
CLI entry point for single USD/JPY analysis |
test_all.py |
Comprehensive QA suite (~90 tests, no live API needed) |
Key Concepts
Signal Pipeline: OHLCV → indicators → regime classification → regime-weighted composite score → MTF agreement check → news/event adjustment → confidence penalties → 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, or ranging via ADX + EMA. Trending boosts EMA weight (2.0x), ranging boosts RSI/BB (2.0x). This is the core edge.
Pair-Specific Strategies: XAU/USD (oversold-bounce: RSI<38 + triple-TF EMA200 confirmation), EUR/USD and GBP/USD (pullback-buy: RSI 30-55 + BB<0.75 + dual-TF EMA200). Each has hour-blocked windows from backtest optimization.
SELL Suppression: Forex SELL signals are suppressed for all pairs except XAU/USD. Backtesting showed 0% win rate for forex shorts.
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 1.2:1.
How to Run
# CLI (single USD/JPY 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
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 |
FB2_EMA_LO / FB2_EMA_HI |
EMA200 distance thresholds (%) | 0.5 / 1.5 |
Dependencies
numpy, pandas, requests, twelvedata, python-telegram-bot, websockets — see requirements.txt.
Data Provider
- Source: Twelve Data REST API via
twelvedataSDK - 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: EUR/USD and XAU/USD only (Grow plan WS limit); GBP/USD and USD/JPY via REST-only scans
Telegram Bot
- Auto-scan: M5 (data-only, no alerts), M15, H1, H4 at candle boundaries + 10s buffer
- WebSocket: Streams live ticks for EUR/USD + XAU/USD; triggers immediate M15 scan on key level breach (swing S/R, BB, EMA200) with 5-min cooldown
- Live dashboard: Edit-in-place message, 30s refresh, shows all pairs/timeframes/prices/setups
- Commands:
/start,/autoscan,/status,/cache,/clearcache,/help - Correlation warning: Flags same-direction signals on EUR/USD + GBP/USD (USD-weakness group)
Backtesting
The backtest/ directory (gitignored) contains pair-specific backtesting scripts, GPU-accelerated optimizers, and historical parquet data. Strategies were optimized on 1-year M15 data. Results drove the current strategy configs in config.py.
Planned: MT5 Auto-Execution
Documented in mt5.md. REST bridge architecture: Linux bot → HTTP → Windows FastAPI server wrapping MetaTrader5 COM. Not yet implemented. Key: MT5Config dataclass, idempotent orders, position-size from bridge-side tick values.
Code Conventions
- Python 3.12, type hints on public methods
- Dataclasses for all config and output structures
SignalStrengthenum (-2 to +2) is the universal signal currency- Indicators stored as
Dict[str, IndicatorSignal]inTechnicalAnalysis - Key levels stored as
Dict[str, float]— prefixedswing_high_,swing_low_,ema_,bb_,atr - Private indicator data attached as
result._rsi_series,result._macd_histogram(underscore prefix, skipped by composite scorer) - Blocking signal computation runs in
ThreadPoolExecutor(max_workers=4)to avoid blocking the async Telegram event loop - Shared
ForexDataProviderinstance across all scans for cache reuse