feat: add 5 dashboard features — dark mode, trade history, backtests, model insights, alerts
- Dark mode: class-based theme toggle with localStorage persistence and flash prevention - Trade History (/trades): paginated table, stats cards, equity curve chart with DB API endpoints - Backtest Viewer (/backtests): log parser for 35 backtest results, sidebar + detail + comparison tabs - Model Insights: dashboard card + dialog showing feature importance, regime distribution, training history - Alert/Signal Log (/alerts): signal stats, filterable table with execution tracking - API: 8 new endpoints with psycopg2 DB connection pool - Dark mode sweep across books page, about dialog, and all dashboard components - Architecture docs rewritten with Mermaid diagrams (23 docs) - README and FEATURES.md rewritten bilingual (Indonesian + English) - main_live.py: write model_metrics.json on startup and retrain Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
buckybonez
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
f7ca8003ce
commit
d93d790428
@@ -0,0 +1,97 @@
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"""
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Database connection pool for Trading Bot API.
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Uses psycopg2 with a simple connection pool.
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"""
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import os
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import logging
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from contextlib import contextmanager
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from typing import Optional
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import psycopg2
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from psycopg2 import pool
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from psycopg2.extras import RealDictCursor
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logger = logging.getLogger(__name__)
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_pool: Optional[pool.SimpleConnectionPool] = None
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def get_db_config() -> dict:
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return {
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"host": os.getenv("DB_HOST", "localhost"),
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"port": int(os.getenv("DB_PORT", "5432")),
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"dbname": os.getenv("DB_NAME", "trading_db"),
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"user": os.getenv("DB_USER", "trading_bot"),
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"password": os.getenv("DB_PASSWORD", "trading_bot_2026"),
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}
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def init_pool(minconn: int = 1, maxconn: int = 5):
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"""Initialize connection pool."""
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global _pool
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if _pool is not None:
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return
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try:
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config = get_db_config()
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_pool = pool.SimpleConnectionPool(minconn, maxconn, **config)
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logger.info("Database pool initialized: %s@%s:%s/%s", config["user"], config["host"], config["port"], config["dbname"])
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except Exception as e:
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logger.warning("Could not initialize DB pool: %s", e)
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_pool = None
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def close_pool():
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"""Close all pool connections."""
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global _pool
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if _pool:
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_pool.closeall()
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_pool = None
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logger.info("Database pool closed")
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@contextmanager
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def get_conn():
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"""Get a connection from the pool (context manager)."""
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if _pool is None:
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raise RuntimeError("Database pool not initialized")
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conn = _pool.getconn()
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try:
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yield conn
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finally:
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_pool.putconn(conn)
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@contextmanager
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def get_cursor(commit: bool = False):
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"""Get a dict cursor from the pool."""
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with get_conn() as conn:
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cursor = conn.cursor(cursor_factory=RealDictCursor)
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try:
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yield cursor
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if commit:
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conn.commit()
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except Exception:
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conn.rollback()
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raise
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finally:
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cursor.close()
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def query(sql: str, params: tuple = (), one: bool = False):
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"""Execute a query and return results as list of dicts."""
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try:
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with get_cursor() as cur:
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cur.execute(sql, params)
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rows = cur.fetchall()
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if one:
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return dict(rows[0]) if rows else None
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return [dict(r) for r in rows]
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except Exception as e:
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logger.error("DB query error: %s", e)
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return None if one else []
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def is_available() -> bool:
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"""Check if DB is available."""
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return _pool is not None
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+320
-7
@@ -2,19 +2,24 @@
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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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Reads from data/bot_status.json (written by main_live.py)
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and from PostgreSQL database for trade history, signals, model data.
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"""
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import json
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import logging
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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 typing import Optional
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from fastapi import FastAPI
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from fastapi import FastAPI, Query
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from fastapi.middleware.cors import CORSMiddleware
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import db
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logger = logging.getLogger(__name__)
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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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@@ -28,6 +33,7 @@ app.add_middleware(
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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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MODEL_METRICS_FILE = Path("/app/data/model_metrics.json")
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# Default empty response
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DEFAULT_STATUS = {
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@@ -67,10 +73,27 @@ DEFAULT_STATUS = {
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}
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# ─── Startup / Shutdown ───
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@app.on_event("startup")
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async def startup():
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try:
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db.init_pool()
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logger.info("DB pool ready")
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except Exception as e:
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logger.warning("DB not available: %s (trade history features disabled)", e)
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@app.on_event("shutdown")
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async def shutdown():
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db.close_pool()
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# ─── Status Endpoints ───
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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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@@ -79,7 +102,6 @@ async def get_status():
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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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@@ -97,7 +119,298 @@ async def get_status():
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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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return {"status": "ok", "bot_running": bot_running, "db_available": db.is_available()}
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# ─── Trade History Endpoints ───
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def _date_filter(field: str, start_date: Optional[str], end_date: Optional[str]):
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"""Build date filter SQL clauses."""
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clauses = []
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params = []
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if start_date:
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clauses.append(f"{field} >= %s")
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params.append(start_date)
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if end_date:
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clauses.append(f"{field} <= %s")
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params.append(end_date + " 23:59:59")
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return clauses, params
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@app.get("/api/trades")
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async def get_trades(
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page: int = Query(1, ge=1),
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limit: int = Query(25, ge=1, le=100),
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direction: str = Query("ALL"),
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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):
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"""Get paginated trade history."""
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if not db.is_available():
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return {"trades": [], "total": 0, "page": page, "limit": limit}
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where = ["closed_at IS NOT NULL"]
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params = []
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if direction and direction != "ALL":
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where.append("direction = %s")
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params.append(direction.upper())
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date_clauses, date_params = _date_filter("closed_at", start_date, end_date)
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where.extend(date_clauses)
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params.extend(date_params)
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where_sql = " AND ".join(where)
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offset = (page - 1) * limit
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count_row = db.query(f"SELECT COUNT(*) as cnt FROM trades WHERE {where_sql}", tuple(params), one=True)
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total = count_row["cnt"] if count_row else 0
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params_with_pagination = params + [limit, offset]
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trades = db.query(
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f"""SELECT id, ticket, direction, entry_price, exit_price, lot_size,
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profit_usd, profit_pips, sl_price, tp_price,
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opened_at, closed_at, exit_reason, confidence,
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regime, session, duration_minutes
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FROM trades
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WHERE {where_sql}
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ORDER BY closed_at DESC
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LIMIT %s OFFSET %s""",
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tuple(params_with_pagination),
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)
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for t in trades:
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for k in ("opened_at", "closed_at"):
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if t.get(k) and hasattr(t[k], "isoformat"):
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t[k] = t[k].isoformat()
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return {"trades": trades, "total": total, "page": page, "limit": limit}
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@app.get("/api/trades/stats")
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async def get_trade_stats(
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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):
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"""Get aggregate trade statistics."""
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if not db.is_available():
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return {"totalTrades": 0, "winRate": 0, "netProfit": 0, "profitFactor": 0, "avgWin": 0, "avgLoss": 0, "bestTrade": 0, "worstTrade": 0}
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where = ["closed_at IS NOT NULL"]
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params = []
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date_clauses, date_params = _date_filter("closed_at", start_date, end_date)
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where.extend(date_clauses)
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params.extend(date_params)
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where_sql = " AND ".join(where)
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row = db.query(
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f"""SELECT
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COUNT(*) as total_trades,
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COUNT(*) FILTER (WHERE profit_usd > 0) as wins,
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COALESCE(SUM(profit_usd), 0) as net_profit,
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COALESCE(SUM(profit_usd) FILTER (WHERE profit_usd > 0), 0) as gross_profit,
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COALESCE(ABS(SUM(profit_usd) FILTER (WHERE profit_usd < 0)), 0.01) as gross_loss,
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COALESCE(AVG(profit_usd) FILTER (WHERE profit_usd > 0), 0) as avg_win,
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COALESCE(AVG(profit_usd) FILTER (WHERE profit_usd < 0), 0) as avg_loss,
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COALESCE(MAX(profit_usd), 0) as best_trade,
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COALESCE(MIN(profit_usd), 0) as worst_trade
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FROM trades WHERE {where_sql}""",
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tuple(params),
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one=True,
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)
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if not row or row["total_trades"] == 0:
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return {"totalTrades": 0, "winRate": 0, "netProfit": 0, "profitFactor": 0, "avgWin": 0, "avgLoss": 0, "bestTrade": 0, "worstTrade": 0}
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return {
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"totalTrades": row["total_trades"],
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"winRate": round(row["wins"] / row["total_trades"] * 100, 1) if row["total_trades"] > 0 else 0,
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"netProfit": round(float(row["net_profit"]), 2),
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"profitFactor": round(float(row["gross_profit"]) / float(row["gross_loss"]), 2),
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"avgWin": round(float(row["avg_win"]), 2),
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"avgLoss": round(float(row["avg_loss"]), 2),
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"bestTrade": round(float(row["best_trade"]), 2),
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"worstTrade": round(float(row["worst_trade"]), 2),
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}
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@app.get("/api/trades/equity-curve")
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async def get_equity_curve(
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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):
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"""Get cumulative equity curve from closed trades."""
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if not db.is_available():
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return {"points": []}
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where = ["closed_at IS NOT NULL"]
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params = []
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date_clauses, date_params = _date_filter("closed_at", start_date, end_date)
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where.extend(date_clauses)
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params.extend(date_params)
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where_sql = " AND ".join(where)
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rows = db.query(
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f"""SELECT closed_at, profit_usd,
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SUM(profit_usd) OVER (ORDER BY closed_at) as cumulative
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FROM trades WHERE {where_sql}
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ORDER BY closed_at ASC""",
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tuple(params),
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)
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points = []
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for r in rows:
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dt = r["closed_at"].isoformat() if hasattr(r["closed_at"], "isoformat") else str(r["closed_at"])
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points.append({
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"time": dt,
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"profit": round(float(r["profit_usd"]), 2),
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"cumulative": round(float(r["cumulative"]), 2),
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})
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return {"points": points}
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# ─── Model Insights Endpoints ───
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@app.get("/api/model/metrics")
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async def get_model_metrics():
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"""Read model metrics from JSON file (written by bot on startup/retrain)."""
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for path in [MODEL_METRICS_FILE, Path("data/model_metrics.json")]:
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if path.exists():
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try:
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return json.loads(path.read_text())
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except (json.JSONDecodeError, OSError):
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continue
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return {"featureImportance": [], "trainAuc": 0, "testAuc": 0, "sampleCount": 0, "updatedAt": None}
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@app.get("/api/model/training-history")
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async def get_training_history():
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"""Get model training run history."""
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if not db.is_available():
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return {"runs": []}
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rows = db.query(
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"""SELECT id, started_at, completed_at, train_auc, test_auc,
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sample_count, features_used, trigger_reason
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FROM training_runs
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ORDER BY started_at DESC
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LIMIT 20"""
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)
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for r in rows:
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for k in ("started_at", "completed_at"):
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if r.get(k) and hasattr(r[k], "isoformat"):
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r[k] = r[k].isoformat()
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return {"runs": rows}
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@app.get("/api/model/regime-distribution")
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async def get_regime_distribution():
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"""Get regime distribution from recent market snapshots."""
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if not db.is_available():
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return {"distribution": []}
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rows = db.query(
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"""SELECT regime, COUNT(*) as count
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FROM market_snapshots
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WHERE snapshot_time > NOW() - INTERVAL '7 days'
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GROUP BY regime
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ORDER BY count DESC"""
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)
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return {"distribution": rows}
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# ─── Signal / Alert Endpoints ───
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@app.get("/api/signals")
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async def get_signals(
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page: int = Query(1, ge=1),
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limit: int = Query(50, ge=1, le=200),
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type: str = Query("ALL"),
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executed: str = Query("all"),
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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):
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"""Get paginated signal/alert history."""
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if not db.is_available():
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return {"signals": [], "total": 0, "page": page, "limit": limit}
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where = ["1=1"]
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params = []
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if type and type != "ALL":
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where.append("signal_type = %s")
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params.append(type.upper())
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if executed == "yes":
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where.append("executed = TRUE")
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elif executed == "no":
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where.append("executed = FALSE")
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date_clauses, date_params = _date_filter("signal_time", start_date, end_date)
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where.extend(date_clauses)
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params.extend(date_params)
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where_sql = " AND ".join(where)
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offset = (page - 1) * limit
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count_row = db.query(f"SELECT COUNT(*) as cnt FROM signals WHERE {where_sql}", tuple(params), one=True)
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total = count_row["cnt"] if count_row else 0
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params_with_pagination = params + [limit, offset]
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signals = db.query(
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f"""SELECT id, signal_time, signal_type, confidence, executed,
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execution_reason, regime, session, smc_signal, ml_signal,
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entry_price, sl_price, tp_price
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FROM signals
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WHERE {where_sql}
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ORDER BY signal_time DESC
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LIMIT %s OFFSET %s""",
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tuple(params_with_pagination),
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)
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for s in signals:
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if s.get("signal_time") and hasattr(s["signal_time"], "isoformat"):
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s["signal_time"] = s["signal_time"].isoformat()
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return {"signals": signals, "total": total, "page": page, "limit": limit}
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@app.get("/api/signals/stats")
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async def get_signal_stats(hours: int = Query(24, ge=1, le=168)):
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"""Get signal statistics for the last N hours."""
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if not db.is_available():
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return {"total": 0, "executed": 0, "executionRate": 0, "avgConfidence": 0, "byType": {}}
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row = db.query(
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"""SELECT
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COUNT(*) as total,
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COUNT(*) FILTER (WHERE executed = TRUE) as executed,
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COALESCE(AVG(confidence), 0) as avg_confidence
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FROM signals
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WHERE signal_time > NOW() - MAKE_INTERVAL(hours => %s)""",
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(hours,),
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one=True,
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)
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if not row or row["total"] == 0:
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return {"total": 0, "executed": 0, "executionRate": 0, "avgConfidence": 0, "byType": {}}
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by_type = db.query(
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"""SELECT signal_type, COUNT(*) as count
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FROM signals
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WHERE signal_time > NOW() - MAKE_INTERVAL(hours => %s)
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GROUP BY signal_type""",
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(hours,),
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)
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return {
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"total": row["total"],
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"executed": row["executed"],
|
||||
"executionRate": round(row["executed"] / row["total"] * 100, 1) if row["total"] > 0 else 0,
|
||||
"avgConfidence": round(float(row["avg_confidence"]), 1),
|
||||
"byType": {r["signal_type"]: r["count"] for r in by_type},
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -1,2 +1,3 @@
|
||||
fastapi>=0.109.0
|
||||
uvicorn[standard]>=0.27.0
|
||||
psycopg2-binary>=2.9.0
|
||||
|
||||
Reference in New Issue
Block a user