GifariKemal
61877480b3
- Create docs/FEATURES.md with complete feature reference (14 entry filters, 12 exit conditions, backtest history, risk modes, session rules, auto-trainer, active components table, architecture diagram) - Extend main_live.py _write_dashboard_status() with 10 new data sections: entryFilters, riskMode, cooldown, timeFilter, sessionMultiplier, positionDetails, autoTrainer, performance, marketClose, h1BiasDetails. Add filter tracking at each checkpoint in _trading_iteration() and 7 helper methods. - Add 9 TypeScript interfaces and extend TradingStatus in trading.ts - Create BotStatusCard (risk mode, cooldown bar, AUC, uptime, market close) and EntryFilterCard (14 filters with pass/block/skip icons) - Enhance SessionCard (lot multiplier badge + time filter status), RiskCard (risk mode badge + total loss progress bar), PositionsCard (expandable per-position details with momentum, TP probability) - Update page.tsx layout: BotStatusCard replaces SettingsCard in Row 2, EntryFilterCard added to Row 3 sidebar - Add API defaults for all new fields Dashboard now monitors 100% of bot features. Verified: Next.js build 0 errors, bot + API + dashboard all run clean, Docker rebuilt OK. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
"""
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FastAPI Backend for Web Dashboard (Docker-compatible)
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=====================================================
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Serves trading bot status data to the web frontend.
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Reads from data/bot_status.json which is written by main_live.py.
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This allows the API to run in Docker without needing MT5 (Windows-only).
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"""
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import json
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from pathlib import Path
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from datetime import datetime
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from zoneinfo import ZoneInfo
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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app = FastAPI(title="Trading Bot API", version="2.0.0")
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# CORS for frontend
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Status file path (mounted as volume in Docker)
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STATUS_FILE = Path("/app/data/bot_status.json")
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# Default empty response
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DEFAULT_STATUS = {
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"timestamp": "00:00:00",
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"connected": False,
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"price": 0.0,
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"spread": 0.0,
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"priceChange": 0.0,
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"priceHistory": [],
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"balance": 0.0,
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"equity": 0.0,
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"profit": 0.0,
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"equityHistory": [],
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"balanceHistory": [],
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"session": "Unknown",
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"isGoldenTime": False,
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"canTrade": False,
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"dailyLoss": 0.0,
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"dailyProfit": 0.0,
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"consecutiveLosses": 0,
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"riskPercent": 0.0,
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"smc": {"signal": "", "confidence": 0.0, "reason": ""},
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"ml": {"signal": "", "confidence": 0.0, "buyProb": 0.0, "sellProb": 0.0},
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"regime": {"name": "", "volatility": 0.0, "confidence": 0.0},
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"positions": [],
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"logs": [],
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"entryFilters": [],
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"riskMode": {"mode": "unknown", "reason": "", "recommendedLot": 0, "maxAllowedLot": 0, "totalLoss": 0, "maxTotalLoss": 0, "remainingDailyRisk": 0},
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"cooldown": {"active": False, "secondsRemaining": 0, "totalSeconds": 150},
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"timeFilter": {"wibHour": 0, "isBlocked": False, "blockedHours": [9, 21]},
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"sessionMultiplier": 1.0,
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"positionDetails": [],
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"autoTrainer": {"lastRetrain": None, "currentAuc": None, "minAucThreshold": 0.65, "hoursSinceRetrain": 0, "nextRetrainHour": 5, "modelsFitted": False},
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"performance": {"loopCount": 0, "avgExecutionMs": 0, "uptimeHours": 0, "totalSessionTrades": 0, "totalSessionProfit": 0},
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"marketClose": {"hoursToDailyClose": 0, "hoursToWeekendClose": 0, "nearWeekend": False, "marketOpen": False},
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"h1BiasDetails": {"bias": "NEUTRAL", "ema20": 0, "price": 0},
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}
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@app.get("/api/status")
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async def get_status():
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"""Get current trading status from bot's status file."""
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# Try local path first (non-Docker), then Docker path
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for path in [STATUS_FILE, Path("data/bot_status.json")]:
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if path.exists():
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try:
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data = json.loads(path.read_text())
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return data
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except (json.JSONDecodeError, OSError):
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continue
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# No status file — bot not running
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now = datetime.now(ZoneInfo("Asia/Jakarta"))
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result = DEFAULT_STATUS.copy()
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result["timestamp"] = now.strftime("%H:%M:%S")
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result["logs"] = [
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{
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"time": now.strftime("%H:%M:%S"),
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"level": "warning",
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"message": "Bot is not running — waiting for bot_status.json",
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}
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]
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return result
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@app.get("/api/health")
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async def health():
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"""Health check endpoint."""
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bot_running = STATUS_FILE.exists() or Path("data/bot_status.json").exists()
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return {"status": "ok", "bot_running": bot_running}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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