diff --git a/.gitignore b/.gitignore index 7d2ab8f..b425a67 100644 --- a/.gitignore +++ b/.gitignore @@ -171,3 +171,7 @@ cython_debug/ ./logs/ *.csv research/regime_results.html +data/backtest_runs.db +.gitignore +live_trading/regime_model.pkl +live_trading/positions.json diff --git a/api/__init__.py b/api/__init__.py new file mode 100644 index 0000000..ab1f3ad --- /dev/null +++ b/api/__init__.py @@ -0,0 +1,3 @@ +""" +FastAPI backend for Lowkey Backtest UI. +""" diff --git a/api/main.py b/api/main.py new file mode 100644 index 0000000..7667f59 --- /dev/null +++ b/api/main.py @@ -0,0 +1,47 @@ +""" +FastAPI application entry point for Lowkey Backtest UI. + +Run with: uvicorn api.main:app --reload +""" +from contextlib import asynccontextmanager + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from api.models.database import init_db +from api.routers import backtest, data, strategies + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Initialize database on startup.""" + init_db() + yield + + +app = FastAPI( + title="Lowkey Backtest API", + description="API for running and analyzing trading strategy backtests", + version="0.1.0", + lifespan=lifespan, +) + +# CORS configuration for local development +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:5173", "http://127.0.0.1:5173"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Register routers +app.include_router(strategies.router, prefix="/api", tags=["strategies"]) +app.include_router(data.router, prefix="/api", tags=["data"]) +app.include_router(backtest.router, prefix="/api", tags=["backtest"]) + + +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "ok", "service": "lowkey-backtest-api"} diff --git a/api/models/__init__.py b/api/models/__init__.py new file mode 100644 index 0000000..164d82a --- /dev/null +++ b/api/models/__init__.py @@ -0,0 +1,3 @@ +""" +Pydantic schemas and database models. +""" diff --git a/api/models/database.py b/api/models/database.py new file mode 100644 index 0000000..aa25495 --- /dev/null +++ b/api/models/database.py @@ -0,0 +1,99 @@ +""" +SQLAlchemy database models and session management for backtest run persistence. +""" +import json +from datetime import datetime, timezone +from pathlib import Path + +from sqlalchemy import JSON, Column, DateTime, Float, Integer, String, Text, create_engine +from sqlalchemy.orm import DeclarativeBase, Session, sessionmaker + +# Database file location +DB_PATH = Path(__file__).parent.parent.parent / "data" / "backtest_runs.db" +DATABASE_URL = f"sqlite:///{DB_PATH}" + +engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False}) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + + +class Base(DeclarativeBase): + """Base class for SQLAlchemy models.""" + pass + + +class BacktestRun(Base): + """ + Persisted backtest run record. + + Stores all information needed to display and compare runs. + """ + __tablename__ = "backtest_runs" + + id = Column(Integer, primary_key=True, autoincrement=True) + run_id = Column(String(36), unique=True, nullable=False, index=True) + + # Configuration + strategy = Column(String(50), nullable=False, index=True) + symbol = Column(String(20), nullable=False, index=True) + exchange = Column(String(20), nullable=False, default="okx") + market_type = Column(String(20), nullable=False) + timeframe = Column(String(10), nullable=False) + leverage = Column(Integer, nullable=False, default=1) + params = Column(JSON, nullable=False, default=dict) + + # Date range + start_date = Column(String(20), nullable=True) + end_date = Column(String(20), nullable=True) + + # Metrics (denormalized for quick listing) + total_return = Column(Float, nullable=False) + benchmark_return = Column(Float, nullable=False, default=0.0) + alpha = Column(Float, nullable=False, default=0.0) + sharpe_ratio = Column(Float, nullable=False) + max_drawdown = Column(Float, nullable=False) + win_rate = Column(Float, nullable=False) + total_trades = Column(Integer, nullable=False) + profit_factor = Column(Float, nullable=True) + total_fees = Column(Float, nullable=False, default=0.0) + total_funding = Column(Float, nullable=False, default=0.0) + liquidation_count = Column(Integer, nullable=False, default=0) + liquidation_loss = Column(Float, nullable=False, default=0.0) + adjusted_return = Column(Float, nullable=True) + + # Full data (JSON serialized) + equity_curve = Column(Text, nullable=False) # JSON array + trades = Column(Text, nullable=False) # JSON array + + # Metadata + created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(timezone.utc)) + + def set_equity_curve(self, data: list[dict]): + """Serialize equity curve to JSON string.""" + self.equity_curve = json.dumps(data) + + def get_equity_curve(self) -> list[dict]: + """Deserialize equity curve from JSON string.""" + return json.loads(self.equity_curve) if self.equity_curve else [] + + def set_trades(self, data: list[dict]): + """Serialize trades to JSON string.""" + self.trades = json.dumps(data) + + def get_trades(self) -> list[dict]: + """Deserialize trades from JSON string.""" + return json.loads(self.trades) if self.trades else [] + + +def init_db(): + """Create database tables if they don't exist.""" + DB_PATH.parent.mkdir(parents=True, exist_ok=True) + Base.metadata.create_all(bind=engine) + + +def get_db() -> Session: + """Get database session (dependency injection).""" + db = SessionLocal() + try: + yield db + finally: + db.close() diff --git a/api/models/schemas.py b/api/models/schemas.py new file mode 100644 index 0000000..f775ad5 --- /dev/null +++ b/api/models/schemas.py @@ -0,0 +1,162 @@ +""" +Pydantic schemas for API request/response models. +""" +from datetime import datetime +from typing import Any + +from pydantic import BaseModel, Field + + +# --- Strategy Schemas --- + +class StrategyParam(BaseModel): + """Single strategy parameter definition.""" + name: str + value: Any + param_type: str = Field(description="Type: int, float, bool, list") + min_value: float | None = None + max_value: float | None = None + description: str | None = None + + +class StrategyInfo(BaseModel): + """Strategy information with parameters.""" + name: str + display_name: str + market_type: str + default_leverage: int + default_params: dict[str, Any] + grid_params: dict[str, Any] + + +class StrategiesResponse(BaseModel): + """Response for GET /api/strategies.""" + strategies: list[StrategyInfo] + + +# --- Symbol/Data Schemas --- + +class SymbolInfo(BaseModel): + """Available symbol information.""" + symbol: str + exchange: str + market_type: str + timeframes: list[str] + start_date: str | None = None + end_date: str | None = None + row_count: int = 0 + + +class DataStatusResponse(BaseModel): + """Response for GET /api/data/status.""" + symbols: list[SymbolInfo] + + +# --- Backtest Schemas --- + +class BacktestRequest(BaseModel): + """Request body for POST /api/backtest.""" + strategy: str + symbol: str + exchange: str = "okx" + timeframe: str = "1h" + market_type: str = "perpetual" + start_date: str | None = None + end_date: str | None = None + init_cash: float = 10000.0 + leverage: int | None = None + fees: float | None = None + slippage: float = 0.001 + sl_stop: float | None = None + tp_stop: float | None = None + sl_trail: bool = False + params: dict[str, Any] = Field(default_factory=dict) + + +class TradeRecord(BaseModel): + """Single trade record.""" + entry_time: str + exit_time: str | None = None + entry_price: float + exit_price: float | None = None + size: float + direction: str + pnl: float | None = None + return_pct: float | None = None + status: str = "closed" + + +class EquityPoint(BaseModel): + """Single point on equity curve.""" + timestamp: str + value: float + drawdown: float = 0.0 + + +class BacktestMetrics(BaseModel): + """Backtest performance metrics.""" + total_return: float + benchmark_return: float = 0.0 + alpha: float = 0.0 + sharpe_ratio: float + max_drawdown: float + win_rate: float + total_trades: int + profit_factor: float | None = None + avg_trade_return: float | None = None + total_fees: float = 0.0 + total_funding: float = 0.0 + liquidation_count: int = 0 + liquidation_loss: float = 0.0 + adjusted_return: float | None = None + + +class BacktestResult(BaseModel): + """Complete backtest result.""" + run_id: str + strategy: str + symbol: str + market_type: str + timeframe: str + start_date: str + end_date: str + leverage: int + params: dict[str, Any] + metrics: BacktestMetrics + equity_curve: list[EquityPoint] + trades: list[TradeRecord] + created_at: str + + +class BacktestSummary(BaseModel): + """Summary for backtest list view.""" + run_id: str + strategy: str + symbol: str + market_type: str + timeframe: str + total_return: float + sharpe_ratio: float + max_drawdown: float + total_trades: int + created_at: str + params: dict[str, Any] + + +class BacktestListResponse(BaseModel): + """Response for GET /api/backtests.""" + runs: list[BacktestSummary] + total: int + + +# --- Comparison Schemas --- + +class CompareRequest(BaseModel): + """Request body for POST /api/compare.""" + run_ids: list[str] = Field(min_length=2, max_length=5) + + +class CompareResult(BaseModel): + """Comparison of multiple backtest runs.""" + runs: list[BacktestResult] + param_diff: dict[str, list[Any]] diff --git a/api/routers/__init__.py b/api/routers/__init__.py new file mode 100644 index 0000000..c1c65af --- /dev/null +++ b/api/routers/__init__.py @@ -0,0 +1,3 @@ +""" +API routers for backtest, strategies, and data endpoints. +""" diff --git a/api/routers/backtest.py b/api/routers/backtest.py new file mode 100644 index 0000000..95e361e --- /dev/null +++ b/api/routers/backtest.py @@ -0,0 +1,193 @@ +""" +Backtest execution and history endpoints. +""" +from typing import Any + +from fastapi import APIRouter, Depends, HTTPException, Query +from sqlalchemy.orm import Session + +from api.models.database import get_db +from api.models.schemas import ( + BacktestListResponse, + BacktestRequest, + BacktestResult, + CompareRequest, + CompareResult, +) +from api.services.runner import get_runner +from api.services.storage import get_storage +from engine.logging_config import get_logger + +router = APIRouter() +logger = get_logger(__name__) + + +@router.post("/backtest", response_model=BacktestResult) +async def run_backtest( + request: BacktestRequest, + db: Session = Depends(get_db), +): + """ + Execute a backtest with the specified configuration. + + Runs the strategy on historical data and returns metrics, + equity curve, and trade records. Results are automatically saved. + """ + runner = get_runner() + storage = get_storage() + + try: + # Execute backtest + result = runner.run(request) + + # Save to database + storage.save_run(db, result) + + logger.info( + "Backtest completed and saved: %s (return=%.2f%%, sharpe=%.2f)", + result.run_id, + result.metrics.total_return, + result.metrics.sharpe_ratio, + ) + + return result + + except KeyError as e: + raise HTTPException(status_code=400, detail=f"Invalid strategy: {e}") + except FileNotFoundError as e: + raise HTTPException(status_code=404, detail=f"Data not found: {e}") + except Exception as e: + logger.error("Backtest failed: %s", e, exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/backtests", response_model=BacktestListResponse) +async def list_backtests( + limit: int = Query(50, ge=1, le=200), + offset: int = Query(0, ge=0), + strategy: str | None = None, + symbol: str | None = None, + db: Session = Depends(get_db), +): + """ + List saved backtest runs with optional filtering. + + Returns summaries for quick display in the history sidebar. + """ + storage = get_storage() + + runs, total = storage.list_runs( + db, + limit=limit, + offset=offset, + strategy=strategy, + symbol=symbol, + ) + + return BacktestListResponse(runs=runs, total=total) + + +@router.get("/backtest/{run_id}", response_model=BacktestResult) +async def get_backtest( + run_id: str, + db: Session = Depends(get_db), +): + """ + Retrieve a specific backtest run by ID. + + Returns full result including equity curve and trades. + """ + storage = get_storage() + + result = storage.get_run(db, run_id) + + if not result: + raise HTTPException(status_code=404, detail=f"Run not found: {run_id}") + + return result + + +@router.delete("/backtest/{run_id}") +async def delete_backtest( + run_id: str, + db: Session = Depends(get_db), +): + """ + Delete a backtest run. + """ + storage = get_storage() + + deleted = storage.delete_run(db, run_id) + + if not deleted: + raise HTTPException(status_code=404, detail=f"Run not found: {run_id}") + + return {"status": "deleted", "run_id": run_id} + + +@router.post("/compare", response_model=CompareResult) +async def compare_runs( + request: CompareRequest, + db: Session = Depends(get_db), +): + """ + Compare multiple backtest runs (2-5 runs). + + Returns full results for each run plus parameter differences. + """ + storage = get_storage() + + runs = storage.get_runs_by_ids(db, request.run_ids) + + if len(runs) != len(request.run_ids): + found_ids = {r.run_id for r in runs} + missing = [rid for rid in request.run_ids if rid not in found_ids] + raise HTTPException( + status_code=404, + detail=f"Runs not found: {missing}" + ) + + # Calculate parameter differences + param_diff = _calculate_param_diff(runs) + + return CompareResult(runs=runs, param_diff=param_diff) + + +def _calculate_param_diff(runs: list[BacktestResult]) -> dict[str, list[Any]]: + """ + Find parameters that differ between runs. + + Returns dict mapping param name to list of values (one per run). + """ + if not runs: + return {} + + # Collect all param keys + all_keys: set[str] = set() + for run in runs: + all_keys.update(run.params.keys()) + + # Also include strategy and key config + all_keys.update(['strategy', 'symbol', 'leverage', 'timeframe']) + + diff: dict[str, list[Any]] = {} + + for key in sorted(all_keys): + values = [] + for run in runs: + if key == 'strategy': + values.append(run.strategy) + elif key == 'symbol': + values.append(run.symbol) + elif key == 'leverage': + values.append(run.leverage) + elif key == 'timeframe': + values.append(run.timeframe) + else: + values.append(run.params.get(key)) + + # Only include if values differ + if len(set(str(v) for v in values)) > 1: + diff[key] = values + + return diff diff --git a/api/routers/data.py b/api/routers/data.py new file mode 100644 index 0000000..7cfa67b --- /dev/null +++ b/api/routers/data.py @@ -0,0 +1,97 @@ +""" +Data status and symbol information endpoints. +""" +from pathlib import Path + +import pandas as pd +from fastapi import APIRouter + +from api.models.schemas import DataStatusResponse, SymbolInfo + +router = APIRouter() + +# Base path for CCXT data +DATA_BASE = Path(__file__).parent.parent.parent / "data" / "ccxt" + + +def _scan_available_data() -> list[SymbolInfo]: + """ + Scan the data directory for available symbols and timeframes. + + Returns list of SymbolInfo with date ranges and row counts. + """ + symbols = [] + + if not DATA_BASE.exists(): + return symbols + + # Structure: data/ccxt/{exchange}/{market_type}/{symbol}/{timeframe}.csv + for exchange_dir in DATA_BASE.iterdir(): + if not exchange_dir.is_dir(): + continue + exchange = exchange_dir.name + + for market_dir in exchange_dir.iterdir(): + if not market_dir.is_dir(): + continue + market_type = market_dir.name + + for symbol_dir in market_dir.iterdir(): + if not symbol_dir.is_dir(): + continue + symbol = symbol_dir.name + + # Find all timeframes + timeframes = [] + start_date = None + end_date = None + row_count = 0 + + for csv_file in symbol_dir.glob("*.csv"): + tf = csv_file.stem + timeframes.append(tf) + + # Read first and last rows for date range + try: + df = pd.read_csv(csv_file, parse_dates=['timestamp']) + if not df.empty: + row_count = len(df) + start_date = df['timestamp'].min().strftime("%Y-%m-%d") + end_date = df['timestamp'].max().strftime("%Y-%m-%d") + except Exception: + pass + + if timeframes: + symbols.append(SymbolInfo( + symbol=symbol, + exchange=exchange, + market_type=market_type, + timeframes=sorted(timeframes), + start_date=start_date, + end_date=end_date, + row_count=row_count, + )) + + return symbols + + +@router.get("/symbols", response_model=DataStatusResponse) +async def get_symbols(): + """ + Get list of available symbols with their data ranges. + + Scans the local data directory for downloaded OHLCV data. + """ + symbols = _scan_available_data() + return DataStatusResponse(symbols=symbols) + + +@router.get("/data/status", response_model=DataStatusResponse) +async def get_data_status(): + """ + Get detailed data inventory status. + + Alias for /symbols with additional metadata. + """ + symbols = _scan_available_data() + return DataStatusResponse(symbols=symbols) diff --git a/api/routers/strategies.py b/api/routers/strategies.py new file mode 100644 index 0000000..5fa4f3c --- /dev/null +++ b/api/routers/strategies.py @@ -0,0 +1,67 @@ +""" +Strategy information endpoints. +""" +from typing import Any + +import numpy as np +from fastapi import APIRouter + +from api.models.schemas import StrategiesResponse, StrategyInfo +from strategies.factory import get_registry + +router = APIRouter() + + +def _serialize_param_value(value: Any) -> Any: + """Convert numpy arrays and other types to JSON-serializable format.""" + if isinstance(value, np.ndarray): + return value.tolist() + if isinstance(value, (np.integer, np.floating)): + return value.item() + return value + + +def _get_display_name(name: str) -> str: + """Convert strategy key to display name.""" + display_names = { + "rsi": "RSI Strategy", + "macross": "MA Crossover", + "meta_st": "Meta Supertrend", + "regime": "Regime Reversion (ML)", + } + return display_names.get(name, name.replace("_", " ").title()) + + +@router.get("/strategies", response_model=StrategiesResponse) +async def get_strategies(): + """ + Get list of available strategies with their parameters. + + Returns strategy names, default parameters, and grid search ranges. + """ + registry = get_registry() + strategies = [] + + for name, config in registry.items(): + strategy_instance = config.strategy_class() + + # Serialize parameters (convert numpy arrays to lists) + default_params = { + k: _serialize_param_value(v) + for k, v in config.default_params.items() + } + grid_params = { + k: _serialize_param_value(v) + for k, v in config.grid_params.items() + } + + strategies.append(StrategyInfo( + name=name, + display_name=_get_display_name(name), + market_type=strategy_instance.default_market_type.value, + default_leverage=strategy_instance.default_leverage, + default_params=default_params, + grid_params=grid_params, + )) + + return StrategiesResponse(strategies=strategies) diff --git a/api/services/__init__.py b/api/services/__init__.py new file mode 100644 index 0000000..34f1c01 --- /dev/null +++ b/api/services/__init__.py @@ -0,0 +1,3 @@ +""" +Business logic services for backtest execution and storage. +""" diff --git a/api/services/runner.py b/api/services/runner.py new file mode 100644 index 0000000..9a0a66b --- /dev/null +++ b/api/services/runner.py @@ -0,0 +1,300 @@ +""" +Backtest runner service that wraps the existing Backtester engine. +""" +import uuid +from datetime import datetime, timezone +from typing import Any + +import pandas as pd + +from api.models.schemas import ( + BacktestMetrics, + BacktestRequest, + BacktestResult, + EquityPoint, + TradeRecord, +) +from engine.backtester import Backtester +from engine.data_manager import DataManager +from engine.logging_config import get_logger +from engine.market import MarketType +from strategies.factory import get_strategy + +logger = get_logger(__name__) + + +class BacktestRunner: + """ + Service for executing backtests via the API. + + Wraps the existing Backtester engine and converts results + to API response format. + """ + + def __init__(self): + self.dm = DataManager() + self.bt = Backtester(self.dm) + + def run(self, request: BacktestRequest) -> BacktestResult: + """ + Execute a backtest and return structured results. + + Args: + request: BacktestRequest with strategy, symbol, and parameters + + Returns: + BacktestResult with metrics, equity curve, and trades + """ + # Get strategy instance + strategy, default_params = get_strategy(request.strategy, is_grid=False) + + # Merge default params with request params + params = {**default_params, **request.params} + + # Convert market type string to enum + market_type = MarketType(request.market_type) + + # Override strategy market type if specified + strategy.default_market_type = market_type + + logger.info( + "Running backtest: %s on %s (%s), params=%s", + request.strategy, request.symbol, request.timeframe, params + ) + + # Execute backtest + result = self.bt.run_strategy( + strategy=strategy, + exchange_id=request.exchange, + symbol=request.symbol, + timeframe=request.timeframe, + start_date=request.start_date, + end_date=request.end_date, + init_cash=request.init_cash, + fees=request.fees, + slippage=request.slippage, + sl_stop=request.sl_stop, + tp_stop=request.tp_stop, + sl_trail=request.sl_trail, + leverage=request.leverage, + **params + ) + + # Extract data from portfolio + portfolio = result.portfolio + + # Build trade records + trades = self._build_trade_records(portfolio) + + # Build equity curve (trimmed to trading period) + equity_curve = self._build_equity_curve(portfolio) + + # Build metrics + metrics = self._build_metrics(result, portfolio) + + # Get date range from actual trading period (first trade to end) + idx = portfolio.wrapper.index + end_date = idx[-1].strftime("%Y-%m-%d %H:%M") + + # Use first trade time as start if trades exist + trades_df = portfolio.trades.records_readable + if not trades_df.empty: + first_entry_col = 'Entry Timestamp' if 'Entry Timestamp' in trades_df.columns else 'Entry Time' + if first_entry_col in trades_df.columns: + first_trade_time = pd.to_datetime(trades_df[first_entry_col].iloc[0]) + start_date = first_trade_time.strftime("%Y-%m-%d %H:%M") + else: + start_date = idx[0].strftime("%Y-%m-%d %H:%M") + else: + start_date = idx[0].strftime("%Y-%m-%d %H:%M") + + return BacktestResult( + run_id=str(uuid.uuid4()), + strategy=request.strategy, + symbol=request.symbol, + market_type=result.market_type.value, + timeframe=request.timeframe, + start_date=start_date, + end_date=end_date, + leverage=result.leverage, + params=params, + metrics=metrics, + equity_curve=equity_curve, + trades=trades, + created_at=datetime.now(timezone.utc).isoformat(), + ) + + def _build_equity_curve(self, portfolio) -> list[EquityPoint]: + """Extract equity curve with drawdown from portfolio, starting from first trade.""" + value_series = portfolio.value() + drawdown_series = portfolio.drawdown() + + # Handle multi-column case (from grid search) + if hasattr(value_series, 'columns') and len(value_series.columns) > 1: + value_series = value_series.iloc[:, 0] + drawdown_series = drawdown_series.iloc[:, 0] + elif hasattr(value_series, 'columns'): + value_series = value_series.iloc[:, 0] + drawdown_series = drawdown_series.iloc[:, 0] + + # Find first trade time to trim equity curve + first_trade_idx = 0 + trades_df = portfolio.trades.records_readable + if not trades_df.empty: + first_entry_col = 'Entry Timestamp' if 'Entry Timestamp' in trades_df.columns else 'Entry Time' + if first_entry_col in trades_df.columns: + first_trade_time = pd.to_datetime(trades_df[first_entry_col].iloc[0]) + # Find index in value_series closest to first trade + if hasattr(value_series.index, 'get_indexer'): + first_trade_idx = value_series.index.get_indexer([first_trade_time], method='nearest')[0] + # Start a few bars before first trade for context + first_trade_idx = max(0, first_trade_idx - 5) + + # Slice from first trade onwards + value_series = value_series.iloc[first_trade_idx:] + drawdown_series = drawdown_series.iloc[first_trade_idx:] + + points = [] + for i, (ts, val) in enumerate(value_series.items()): + dd = drawdown_series.iloc[i] if i < len(drawdown_series) else 0.0 + points.append(EquityPoint( + timestamp=ts.isoformat(), + value=float(val), + drawdown=float(dd) * 100, # Convert to percentage + )) + + return points + + def _build_trade_records(self, portfolio) -> list[TradeRecord]: + """Extract trade records from portfolio.""" + trades_df = portfolio.trades.records_readable + + if trades_df.empty: + return [] + + records = [] + for _, row in trades_df.iterrows(): + # Handle different column names in vectorbt + entry_time = row.get('Entry Timestamp', row.get('Entry Time', '')) + exit_time = row.get('Exit Timestamp', row.get('Exit Time', '')) + + records.append(TradeRecord( + entry_time=str(entry_time) if pd.notna(entry_time) else "", + exit_time=str(exit_time) if pd.notna(exit_time) else None, + entry_price=float(row.get('Avg Entry Price', row.get('Entry Price', 0))), + exit_price=float(row.get('Avg Exit Price', row.get('Exit Price', 0))) + if pd.notna(row.get('Avg Exit Price', row.get('Exit Price'))) else None, + size=float(row.get('Size', 0)), + direction=str(row.get('Direction', 'Long')), + pnl=float(row.get('PnL', 0)) if pd.notna(row.get('PnL')) else None, + return_pct=float(row.get('Return', 0)) * 100 + if pd.notna(row.get('Return')) else None, + status="closed" if pd.notna(exit_time) else "open", + )) + + return records + + def _build_metrics(self, result, portfolio) -> BacktestMetrics: + """Build metrics from backtest result.""" + stats = portfolio.stats() + + # Extract values, handling potential multi-column results + def get_stat(key: str, default: float = 0.0) -> float: + val = stats.get(key, default) + if hasattr(val, 'mean'): + return float(val.mean()) + return float(val) if pd.notna(val) else default + + total_return = portfolio.total_return() + if hasattr(total_return, 'mean'): + total_return = total_return.mean() + + # Calculate benchmark return from first trade to end (not full period) + # This gives accurate comparison when strategy has training period + close = portfolio.close + benchmark_return = 0.0 + + if hasattr(close, 'iloc'): + # Find first trade entry time + trades_df = portfolio.trades.records_readable + if not trades_df.empty: + # Get the first trade entry timestamp + first_entry_col = 'Entry Timestamp' if 'Entry Timestamp' in trades_df.columns else 'Entry Time' + if first_entry_col in trades_df.columns: + first_trade_time = pd.to_datetime(trades_df[first_entry_col].iloc[0]) + + # Find the price at first trade + if hasattr(close.index, 'get_indexer'): + # Find closest index to first trade time + idx = close.index.get_indexer([first_trade_time], method='nearest')[0] + start_price = close.iloc[idx] + else: + start_price = close.iloc[0] + + end_price = close.iloc[-1] + + if hasattr(start_price, 'mean'): + start_price = start_price.mean() + if hasattr(end_price, 'mean'): + end_price = end_price.mean() + + benchmark_return = ((end_price - start_price) / start_price) + else: + # No trades - use full period + start_price = close.iloc[0] + end_price = close.iloc[-1] + if hasattr(start_price, 'mean'): + start_price = start_price.mean() + if hasattr(end_price, 'mean'): + end_price = end_price.mean() + benchmark_return = ((end_price - start_price) / start_price) + + # Alpha = strategy return - benchmark return + alpha = float(total_return) - float(benchmark_return) + + sharpe = portfolio.sharpe_ratio() + if hasattr(sharpe, 'mean'): + sharpe = sharpe.mean() + + max_dd = portfolio.max_drawdown() + if hasattr(max_dd, 'mean'): + max_dd = max_dd.mean() + + win_rate = portfolio.trades.win_rate() + if hasattr(win_rate, 'mean'): + win_rate = win_rate.mean() + + trade_count = portfolio.trades.count() + if hasattr(trade_count, 'mean'): + trade_count = int(trade_count.mean()) + else: + trade_count = int(trade_count) + + return BacktestMetrics( + total_return=float(total_return) * 100, + benchmark_return=float(benchmark_return) * 100, + alpha=float(alpha) * 100, + sharpe_ratio=float(sharpe) if pd.notna(sharpe) else 0.0, + max_drawdown=float(max_dd) * 100, + win_rate=float(win_rate) * 100 if pd.notna(win_rate) else 0.0, + total_trades=trade_count, + profit_factor=get_stat('Profit Factor'), + avg_trade_return=get_stat('Avg Winning Trade [%]'), + total_fees=get_stat('Total Fees Paid'), + total_funding=result.total_funding_paid, + liquidation_count=result.liquidation_count, + liquidation_loss=result.total_liquidation_loss, + adjusted_return=result.adjusted_return, + ) + + +# Singleton instance +_runner: BacktestRunner | None = None + + +def get_runner() -> BacktestRunner: + """Get or create the backtest runner instance.""" + global _runner + if _runner is None: + _runner = BacktestRunner() + return _runner diff --git a/api/services/storage.py b/api/services/storage.py new file mode 100644 index 0000000..115ddfa --- /dev/null +++ b/api/services/storage.py @@ -0,0 +1,225 @@ +""" +Storage service for persisting and retrieving backtest runs. +""" +from sqlalchemy.orm import Session + +from api.models.database import BacktestRun +from api.models.schemas import ( + BacktestResult, + BacktestSummary, + EquityPoint, + BacktestMetrics, + TradeRecord, +) + + +class StorageService: + """ + Service for saving and loading backtest runs from SQLite. + """ + + def save_run(self, db: Session, result: BacktestResult) -> BacktestRun: + """ + Persist a backtest result to the database. + + Args: + db: Database session + result: BacktestResult to save + + Returns: + Created BacktestRun record + """ + run = BacktestRun( + run_id=result.run_id, + strategy=result.strategy, + symbol=result.symbol, + market_type=result.market_type, + timeframe=result.timeframe, + leverage=result.leverage, + params=result.params, + start_date=result.start_date, + end_date=result.end_date, + total_return=result.metrics.total_return, + benchmark_return=result.metrics.benchmark_return, + alpha=result.metrics.alpha, + sharpe_ratio=result.metrics.sharpe_ratio, + max_drawdown=result.metrics.max_drawdown, + win_rate=result.metrics.win_rate, + total_trades=result.metrics.total_trades, + profit_factor=result.metrics.profit_factor, + total_fees=result.metrics.total_fees, + total_funding=result.metrics.total_funding, + liquidation_count=result.metrics.liquidation_count, + liquidation_loss=result.metrics.liquidation_loss, + adjusted_return=result.metrics.adjusted_return, + ) + + # Serialize complex data + run.set_equity_curve([p.model_dump() for p in result.equity_curve]) + run.set_trades([t.model_dump() for t in result.trades]) + + db.add(run) + db.commit() + db.refresh(run) + + return run + + def get_run(self, db: Session, run_id: str) -> BacktestResult | None: + """ + Retrieve a backtest run by ID. + + Args: + db: Database session + run_id: UUID of the run + + Returns: + BacktestResult or None if not found + """ + run = db.query(BacktestRun).filter(BacktestRun.run_id == run_id).first() + + if not run: + return None + + return self._to_result(run) + + def list_runs( + self, + db: Session, + limit: int = 50, + offset: int = 0, + strategy: str | None = None, + symbol: str | None = None, + ) -> tuple[list[BacktestSummary], int]: + """ + List backtest runs with optional filtering. + + Args: + db: Database session + limit: Maximum number of runs to return + offset: Offset for pagination + strategy: Filter by strategy name + symbol: Filter by symbol + + Returns: + Tuple of (list of summaries, total count) + """ + query = db.query(BacktestRun) + + if strategy: + query = query.filter(BacktestRun.strategy == strategy) + if symbol: + query = query.filter(BacktestRun.symbol == symbol) + + total = query.count() + + runs = query.order_by(BacktestRun.created_at.desc()).offset(offset).limit(limit).all() + + summaries = [self._to_summary(run) for run in runs] + + return summaries, total + + def get_runs_by_ids(self, db: Session, run_ids: list[str]) -> list[BacktestResult]: + """ + Retrieve multiple runs by their IDs. + + Args: + db: Database session + run_ids: List of run UUIDs + + Returns: + List of BacktestResults (preserves order) + """ + runs = db.query(BacktestRun).filter(BacktestRun.run_id.in_(run_ids)).all() + + # Create lookup and preserve order + run_map = {run.run_id: run for run in runs} + results = [] + + for run_id in run_ids: + if run_id in run_map: + results.append(self._to_result(run_map[run_id])) + + return results + + def delete_run(self, db: Session, run_id: str) -> bool: + """ + Delete a backtest run. + + Args: + db: Database session + run_id: UUID of the run to delete + + Returns: + True if deleted, False if not found + """ + run = db.query(BacktestRun).filter(BacktestRun.run_id == run_id).first() + + if not run: + return False + + db.delete(run) + db.commit() + return True + + def _to_result(self, run: BacktestRun) -> BacktestResult: + """Convert database record to BacktestResult schema.""" + equity_data = run.get_equity_curve() + trades_data = run.get_trades() + + return BacktestResult( + run_id=run.run_id, + strategy=run.strategy, + symbol=run.symbol, + market_type=run.market_type, + timeframe=run.timeframe, + start_date=run.start_date or "", + end_date=run.end_date or "", + leverage=run.leverage, + params=run.params or {}, + metrics=BacktestMetrics( + total_return=run.total_return, + benchmark_return=run.benchmark_return or 0.0, + alpha=run.alpha or 0.0, + sharpe_ratio=run.sharpe_ratio, + max_drawdown=run.max_drawdown, + win_rate=run.win_rate, + total_trades=run.total_trades, + profit_factor=run.profit_factor, + total_fees=run.total_fees, + total_funding=run.total_funding, + liquidation_count=run.liquidation_count, + liquidation_loss=run.liquidation_loss, + adjusted_return=run.adjusted_return, + ), + equity_curve=[EquityPoint(**p) for p in equity_data], + trades=[TradeRecord(**t) for t in trades_data], + created_at=run.created_at.isoformat() if run.created_at else "", + ) + + def _to_summary(self, run: BacktestRun) -> BacktestSummary: + """Convert database record to BacktestSummary schema.""" + return BacktestSummary( + run_id=run.run_id, + strategy=run.strategy, + symbol=run.symbol, + market_type=run.market_type, + timeframe=run.timeframe, + total_return=run.total_return, + sharpe_ratio=run.sharpe_ratio, + max_drawdown=run.max_drawdown, + total_trades=run.total_trades, + created_at=run.created_at.isoformat() if run.created_at else "", + params=run.params or {}, + ) + + +# Singleton instance +_storage: StorageService | None = None + + +def get_storage() -> StorageService: + """Get or create the storage service instance.""" + global _storage + if _storage is None: + _storage = StorageService() + return _storage diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/.vscode/extensions.json b/frontend/.vscode/extensions.json new file mode 100644 index 0000000..a7cea0b --- /dev/null +++ b/frontend/.vscode/extensions.json @@ -0,0 +1,3 @@ +{ + "recommendations": ["Vue.volar"] +} diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..33895ab --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,5 @@ +# Vue 3 + TypeScript + Vite + +This template should help get you started developing with Vue 3 and TypeScript in Vite. 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"axios": "^1.13.2", + "plotly.js-dist-min": "^3.3.1", + "vue": "^3.5.24", + "vue-router": "^4.6.4" + }, + "devDependencies": { + "@tailwindcss/vite": "^4.1.18", + "@types/node": "^24.10.1", + "@vitejs/plugin-vue": "^6.0.1", + "@vue/tsconfig": "^0.8.1", + "tailwindcss": "^4.1.18", + "typescript": "~5.9.3", + "vite": "^7.2.4", + "vue-tsc": "^3.1.4" + } +} diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 0000000..e7b8dfb --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.vue b/frontend/src/App.vue new file mode 100644 index 0000000..c4924b5 --- /dev/null +++ b/frontend/src/App.vue @@ -0,0 +1,72 @@ + + + diff --git a/frontend/src/api/client.ts b/frontend/src/api/client.ts new file mode 100644 index 0000000..ffad890 --- /dev/null +++ b/frontend/src/api/client.ts @@ -0,0 +1,81 @@ +/** + * API client for Lowkey Backtest backend. + */ +import axios from 'axios' +import type { + StrategiesResponse, + DataStatusResponse, + BacktestRequest, + BacktestResult, + BacktestListResponse, + CompareResult, +} from './types' + +const api = axios.create({ + baseURL: '/api', + headers: { + 'Content-Type': 'application/json', + }, +}) + +/** + * Get list of available strategies with parameters. + */ +export async function getStrategies(): Promise { + const response = await api.get('/strategies') + return response.data +} + +/** + * Get list of available symbols with data status. + */ +export async function getSymbols(): Promise { + const response = await api.get('/symbols') + return response.data +} + +/** + * Run a backtest with the given configuration. + */ +export async function runBacktest(request: BacktestRequest): Promise { + const response = await api.post('/backtest', request) + return response.data +} + +/** + * Get list of saved backtest runs. + */ +export async function getBacktests(params?: { + limit?: number + offset?: number + strategy?: string + symbol?: string +}): Promise { + const response = await api.get('/backtests', { params }) + return response.data +} + +/** + * Get a specific backtest run by ID. + */ +export async function getBacktest(runId: string): Promise { + const response = await api.get(`/backtest/${runId}`) + return response.data +} + +/** + * Delete a backtest run. + */ +export async function deleteBacktest(runId: string): Promise { + await api.delete(`/backtest/${runId}`) +} + +/** + * Compare multiple backtest runs. + */ +export async function compareRuns(runIds: string[]): Promise { + const response = await api.post('/compare', { run_ids: runIds }) + return response.data +} + +export default api diff --git a/frontend/src/api/types.ts b/frontend/src/api/types.ts new file mode 100644 index 0000000..1acdf41 --- /dev/null +++ b/frontend/src/api/types.ts @@ -0,0 +1,131 @@ +/** + * TypeScript types matching the FastAPI Pydantic schemas. + */ + +// Strategy types +export interface StrategyInfo { + name: string + display_name: string + market_type: string + default_leverage: number + default_params: Record + grid_params: Record +} + +export interface StrategiesResponse { + strategies: StrategyInfo[] +} + +// Symbol/Data types +export interface SymbolInfo { + symbol: string + exchange: string + market_type: string + timeframes: string[] + start_date: string | null + end_date: string | null + row_count: number +} + +export interface DataStatusResponse { + symbols: SymbolInfo[] +} + +// Backtest types +export interface BacktestRequest { + strategy: string + symbol: string + exchange?: string + timeframe?: string + market_type?: string + start_date?: string | null + end_date?: string | null + init_cash?: number + leverage?: number | null + fees?: number | null + slippage?: number + sl_stop?: number | null + tp_stop?: number | null + sl_trail?: boolean + params?: Record +} + +export interface TradeRecord { + entry_time: string + exit_time: string | null + entry_price: number + exit_price: number | null + size: number + direction: string + pnl: number | null + return_pct: number | null + status: string +} + +export interface EquityPoint { + timestamp: string + value: number + drawdown: number +} + +export interface BacktestMetrics { + total_return: number + benchmark_return: number + alpha: number + sharpe_ratio: number + max_drawdown: number + win_rate: number + total_trades: number + profit_factor: number | null + avg_trade_return: number | null + total_fees: number + total_funding: number + liquidation_count: number + liquidation_loss: number + adjusted_return: number | null +} + +export interface BacktestResult { + run_id: string + strategy: string + symbol: string + market_type: string + timeframe: string + start_date: string + end_date: string + leverage: number + params: Record + metrics: BacktestMetrics + equity_curve: EquityPoint[] + trades: TradeRecord[] + created_at: string +} + +export interface BacktestSummary { + run_id: string + strategy: string + symbol: string + market_type: string + timeframe: string + total_return: number + sharpe_ratio: number + max_drawdown: number + total_trades: number + created_at: string + params: Record +} + +export interface BacktestListResponse { + runs: BacktestSummary[] + total: number +} + +// Comparison types +export interface CompareRequest { + run_ids: string[] +} + +export interface CompareResult { + runs: BacktestResult[] + param_diff: Record +} diff --git a/frontend/src/assets/vue.svg b/frontend/src/assets/vue.svg new file mode 100644 index 0000000..770e9d3 --- /dev/null +++ b/frontend/src/assets/vue.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/components/BacktestConfig.vue b/frontend/src/components/BacktestConfig.vue new file mode 100644 index 0000000..e598f42 --- /dev/null +++ b/frontend/src/components/BacktestConfig.vue @@ -0,0 +1,186 @@ + + + diff --git a/frontend/src/components/EquityCurve.vue b/frontend/src/components/EquityCurve.vue new file mode 100644 index 0000000..5da3015 --- /dev/null +++ b/frontend/src/components/EquityCurve.vue @@ -0,0 +1,88 @@ + + + diff --git a/frontend/src/components/HelloWorld.vue b/frontend/src/components/HelloWorld.vue new file mode 100644 index 0000000..b58e52b --- /dev/null +++ b/frontend/src/components/HelloWorld.vue @@ -0,0 +1,41 @@ + + + + + diff --git a/frontend/src/components/MetricsPanel.vue b/frontend/src/components/MetricsPanel.vue new file mode 100644 index 0000000..a86badc --- /dev/null +++ b/frontend/src/components/MetricsPanel.vue @@ -0,0 +1,144 @@ + + + diff --git a/frontend/src/components/RunHistory.vue b/frontend/src/components/RunHistory.vue new file mode 100644 index 0000000..32f730b --- /dev/null +++ b/frontend/src/components/RunHistory.vue @@ -0,0 +1,141 @@ + + + diff --git a/frontend/src/components/TradeLog.vue b/frontend/src/components/TradeLog.vue new file mode 100644 index 0000000..211cdd4 --- /dev/null +++ b/frontend/src/components/TradeLog.vue @@ -0,0 +1,157 @@ + + + diff --git a/frontend/src/composables/useBacktest.ts b/frontend/src/composables/useBacktest.ts new file mode 100644 index 0000000..e1bbaec --- /dev/null +++ b/frontend/src/composables/useBacktest.ts @@ -0,0 +1,150 @@ +/** + * Composable for managing backtest state across components. + */ +import { ref, computed } from 'vue' +import type { BacktestResult, BacktestSummary, StrategyInfo, SymbolInfo } from '@/api/types' +import { getStrategies, getSymbols, getBacktests, getBacktest, runBacktest, deleteBacktest } from '@/api/client' +import type { BacktestRequest } from '@/api/types' + +// Shared state +const strategies = ref([]) +const symbols = ref([]) +const runs = ref([]) +const currentResult = ref(null) +const selectedRuns = ref([]) +const loading = ref(false) +const error = ref(null) + +// Computed +const symbolsByMarket = computed(() => { + const grouped: Record = {} + for (const s of symbols.value) { + const key = `${s.market_type}` + if (!grouped[key]) grouped[key] = [] + grouped[key].push(s) + } + return grouped +}) + +export function useBacktest() { + /** + * Load strategies and symbols on app init. + */ + async function init() { + try { + const [stratRes, symRes] = await Promise.all([ + getStrategies(), + getSymbols(), + ]) + strategies.value = stratRes.strategies + symbols.value = symRes.symbols + } catch (e) { + error.value = `Failed to load initial data: ${e}` + } + } + + /** + * Refresh the run history list. + */ + async function refreshRuns() { + try { + const res = await getBacktests({ limit: 100 }) + runs.value = res.runs + } catch (e) { + error.value = `Failed to load runs: ${e}` + } + } + + /** + * Execute a new backtest. + */ + async function executeBacktest(request: BacktestRequest) { + loading.value = true + error.value = null + try { + const result = await runBacktest(request) + currentResult.value = result + await refreshRuns() + return result + } catch (e: unknown) { + const msg = e instanceof Error ? e.message : String(e) + error.value = `Backtest failed: ${msg}` + throw e + } finally { + loading.value = false + } + } + + /** + * Load a specific run by ID. + */ + async function loadRun(runId: string) { + loading.value = true + error.value = null + try { + const result = await getBacktest(runId) + currentResult.value = result + return result + } catch (e) { + error.value = `Failed to load run: ${e}` + throw e + } finally { + loading.value = false + } + } + + /** + * Delete a run. + */ + async function removeRun(runId: string) { + try { + await deleteBacktest(runId) + await refreshRuns() + if (currentResult.value?.run_id === runId) { + currentResult.value = null + } + selectedRuns.value = selectedRuns.value.filter(id => id !== runId) + } catch (e) { + error.value = `Failed to delete run: ${e}` + } + } + + /** + * Toggle run selection for comparison. + */ + function toggleRunSelection(runId: string) { + const idx = selectedRuns.value.indexOf(runId) + if (idx >= 0) { + selectedRuns.value.splice(idx, 1) + } else if (selectedRuns.value.length < 5) { + selectedRuns.value.push(runId) + } + } + + /** + * Clear all selections. + */ + function clearSelections() { + selectedRuns.value = [] + } + + return { + // State + strategies, + symbols, + symbolsByMarket, + runs, + currentResult, + selectedRuns, + loading, + error, + // Actions + init, + refreshRuns, + executeBacktest, + loadRun, + removeRun, + toggleRunSelection, + clearSelections, + } +} diff --git a/frontend/src/main.ts b/frontend/src/main.ts new file mode 100644 index 0000000..55a12eb --- /dev/null +++ b/frontend/src/main.ts @@ -0,0 +1,8 @@ +import { createApp } from 'vue' +import App from './App.vue' +import router from './router' +import './style.css' + +const app = createApp(App) +app.use(router) +app.mount('#app') diff --git a/frontend/src/router/index.ts b/frontend/src/router/index.ts new file mode 100644 index 0000000..b246ae3 --- /dev/null +++ b/frontend/src/router/index.ts @@ -0,0 +1,21 @@ +import { createRouter, createWebHistory } from 'vue-router' +import DashboardView from '@/views/DashboardView.vue' +import CompareView from '@/views/CompareView.vue' + +const router = createRouter({ + history: createWebHistory(import.meta.env.BASE_URL), + routes: [ + { + path: '/', + name: 'dashboard', + component: DashboardView, + }, + { + path: '/compare', + name: 'compare', + component: CompareView, + }, + ], +}) + +export default router diff --git a/frontend/src/style.css b/frontend/src/style.css new file mode 100644 index 0000000..7d7d78c --- /dev/null +++ b/frontend/src/style.css @@ -0,0 +1,198 @@ +@import "tailwindcss"; + +/* QuantConnect-inspired dark theme */ +@theme { + /* Background colors */ + --color-bg-primary: #0d1117; + --color-bg-secondary: #161b22; + --color-bg-tertiary: #21262d; + --color-bg-card: #1c2128; + --color-bg-hover: #30363d; + + /* Text colors */ + --color-text-primary: #e6edf3; + --color-text-secondary: #8b949e; + --color-text-muted: #6e7681; + + /* Accent colors */ + --color-accent-blue: #58a6ff; + --color-accent-purple: #a371f7; + --color-accent-cyan: #39d4e8; + + /* Status colors */ + --color-profit: #3fb950; + --color-loss: #f85149; + --color-warning: #d29922; + + /* Border colors */ + --color-border: #30363d; + --color-border-muted: #21262d; + + /* Chart colors for comparison */ + --color-chart-1: #58a6ff; + --color-chart-2: #a371f7; + --color-chart-3: #39d4e8; + --color-chart-4: #f0883e; + --color-chart-5: #db61a2; +} + +/* Base styles */ +body { + background-color: var(--color-bg-primary); + color: var(--color-text-primary); + font-family: 'JetBrains Mono', 'Fira Code', 'SF Mono', Consolas, monospace; +} + +/* Scrollbar styling */ +::-webkit-scrollbar { + width: 8px; + height: 8px; +} + +::-webkit-scrollbar-track { + background: var(--color-bg-secondary); +} + +::-webkit-scrollbar-thumb { + background: var(--color-bg-hover); + border-radius: 4px; +} + +::-webkit-scrollbar-thumb:hover { + background: var(--color-border); +} + +/* Input styling */ +input[type="number"], +input[type="text"], +select { + background-color: var(--color-bg-tertiary); + border: 1px solid var(--color-border); + color: var(--color-text-primary); + border-radius: 6px; + padding: 0.5rem 0.75rem; + font-size: 0.875rem; + transition: border-color 0.2s, box-shadow 0.2s; +} + +input[type="number"]:focus, +input[type="text"]:focus, +select:focus { + outline: none; + border-color: var(--color-accent-blue); + box-shadow: 0 0 0 3px rgba(88, 166, 255, 0.2); +} + +/* Button base */ +.btn { + display: inline-flex; + align-items: center; + justify-content: center; + gap: 0.5rem; + padding: 0.5rem 1rem; + font-size: 0.875rem; + font-weight: 500; + border-radius: 6px; + transition: all 0.2s; + cursor: pointer; + border: 1px solid transparent; +} + +.btn-primary { + background-color: var(--color-accent-blue); + color: #000; +} + +.btn-primary:hover { + background-color: #79b8ff; +} + +.btn-primary:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.btn-secondary { + background-color: var(--color-bg-tertiary); + border-color: var(--color-border); + color: var(--color-text-primary); +} + +.btn-secondary:hover { + background-color: var(--color-bg-hover); +} + +/* Card styling */ +.card { + background-color: var(--color-bg-card); + border: 1px solid var(--color-border-muted); + border-radius: 8px; + padding: 1rem; +} + +/* Profit/Loss coloring */ +.profit { + color: var(--color-profit); +} + +.loss { + color: var(--color-loss); +} + +/* Metric value styling */ +.metric-value { + font-size: 1.5rem; + font-weight: 600; + font-variant-numeric: tabular-nums; +} + +.metric-label { + font-size: 0.75rem; + color: var(--color-text-secondary); + text-transform: uppercase; + letter-spacing: 0.05em; +} + +/* Table styling */ +table { + width: 100%; + border-collapse: collapse; +} + +th { + text-align: left; + padding: 0.75rem; + font-size: 0.75rem; + font-weight: 500; + color: var(--color-text-secondary); + text-transform: uppercase; + letter-spacing: 0.05em; + border-bottom: 1px solid var(--color-border); +} + +td { + padding: 0.75rem; + font-size: 0.875rem; + border-bottom: 1px solid var(--color-border-muted); + font-variant-numeric: tabular-nums; +} + +tr:hover td { + background-color: var(--color-bg-hover); +} + +/* Loading spinner */ +.spinner { + width: 20px; + height: 20px; + border: 2px solid var(--color-border); + border-top-color: var(--color-accent-blue); + border-radius: 50%; + animation: spin 0.8s linear infinite; +} + +@keyframes spin { + to { + transform: rotate(360deg); + } +} diff --git a/frontend/src/types/plotly.d.ts b/frontend/src/types/plotly.d.ts new file mode 100644 index 0000000..e0feb09 --- /dev/null +++ b/frontend/src/types/plotly.d.ts @@ -0,0 +1,4 @@ +declare module 'plotly.js-dist-min' { + import Plotly from 'plotly.js' + export default Plotly +} diff --git a/frontend/src/views/CompareView.vue b/frontend/src/views/CompareView.vue new file mode 100644 index 0000000..685e87f --- /dev/null +++ b/frontend/src/views/CompareView.vue @@ -0,0 +1,362 @@ + + + diff --git a/frontend/src/views/DashboardView.vue b/frontend/src/views/DashboardView.vue new file mode 100644 index 0000000..26ce75f --- /dev/null +++ b/frontend/src/views/DashboardView.vue @@ -0,0 +1,136 @@ + + + diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json new file mode 100644 index 0000000..44b0bf6 --- /dev/null +++ b/frontend/tsconfig.app.json @@ -0,0 +1,20 @@ +{ + "extends": "@vue/tsconfig/tsconfig.dom.json", + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "types": ["vite/client"], + "baseUrl": ".", + "paths": { + "@/*": ["./src/*"] + }, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src/**/*.ts", "src/**/*.tsx", "src/**/*.vue"] +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 0000000..1ffef60 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json new file mode 100644 index 0000000..8a67f62 --- /dev/null +++ b/frontend/tsconfig.node.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "ES2023", + "lib": ["ES2023"], + "module": "ESNext", + "types": ["node"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts new file mode 100644 index 0000000..32922a0 --- /dev/null +++ b/frontend/vite.config.ts @@ -0,0 +1,22 @@ +import { defineConfig } from 'vite' +import vue from '@vitejs/plugin-vue' +import tailwindcss from '@tailwindcss/vite' +import { fileURLToPath, URL } from 'node:url' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [vue(), tailwindcss()], + resolve: { + alias: { + '@': fileURLToPath(new URL('./src', import.meta.url)) + } + }, + server: { + proxy: { + '/api': { + target: 'http://127.0.0.1:8000', + changeOrigin: true + } + } + } +}) diff --git a/live_trading/README.md b/live_trading/README.md new file mode 100644 index 0000000..92122d7 --- /dev/null +++ b/live_trading/README.md @@ -0,0 +1,135 @@ +# Live Trading - Regime Reversion Strategy + +This module implements live trading for the ML-based regime detection and mean reversion strategy on OKX perpetual futures. + +## Overview + +The strategy trades ETH perpetual futures based on: +1. **BTC/ETH Spread Z-Score**: Identifies when ETH is cheap or expensive relative to BTC +2. **Random Forest ML Model**: Predicts probability of successful mean reversion +3. **Funding Rate Filter**: Avoids trades in overheated/oversold market conditions + +## Setup + +### 1. API Keys + +The bot loads OKX API credentials from `../BTC_spot_MVRV/.env`. + +**IMPORTANT: OKX uses SEPARATE API keys for live vs demo trading!** + +#### Option A: Demo Trading (Recommended for Testing) +1. Go to [OKX Demo Trading](https://www.okx.com/demo-trading) +2. Create a demo account if you haven't +3. Generate API keys from the demo environment +4. Set in `.env`: +```env +OKX_API_KEY=your_demo_api_key +OKX_SECRET=your_demo_secret +OKX_PASSWORD=your_demo_passphrase +OKX_DEMO_MODE=true +``` + +#### Option B: Live Trading (Real Funds) +Use your existing live API keys with: +```env +OKX_API_KEY=your_live_api_key +OKX_SECRET=your_live_secret +OKX_PASSWORD=your_live_passphrase +OKX_DEMO_MODE=false +``` + +**Note:** You cannot use live API keys with `OKX_DEMO_MODE=true` or vice versa. +OKX will return error `50101: APIKey does not match current environment`. + +### 2. Dependencies + +All dependencies are already in the project's `pyproject.toml`. No additional installation needed. + +## Usage + +### Run with Demo Account (Recommended First) + +```bash +cd /path/to/lowkey_backtest +uv run python -m live_trading.main +``` + +### Command Line Options + +```bash +# Custom position size +uv run python -m live_trading.main --max-position 500 + +# Custom leverage +uv run python -m live_trading.main --leverage 2 + +# Custom cycle interval (in seconds) +uv run python -m live_trading.main --interval 1800 + +# Combine options +uv run python -m live_trading.main --max-position 1000 --leverage 3 --interval 3600 +``` + +### Live Trading (Use with Caution) + +```bash +# Requires OKX_DEMO_MODE=false in .env +uv run python -m live_trading.main --live +``` + +## Architecture + +``` +live_trading/ + __init__.py # Module initialization + config.py # Configuration loading + okx_client.py # OKX API wrapper + data_feed.py # Real-time OHLCV data + position_manager.py # Position tracking + live_regime_strategy.py # Strategy logic + main.py # Entry point + .env.example # Environment template + README.md # This file +``` + +## Strategy Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `z_entry_threshold` | 1.0 | Enter when \|Z-Score\| > threshold | +| `z_window` | 24 | Rolling window for Z-Score (hours) | +| `model_prob_threshold` | 0.5 | ML probability threshold for entry | +| `funding_threshold` | 0.005 | Funding rate filter threshold | +| `stop_loss_pct` | 6% | Stop-loss percentage | +| `take_profit_pct` | 5% | Take-profit percentage | + +## Files Generated + +- `live_trading/positions.json` - Open positions persistence +- `live_trading/trade_log.csv` - Trade history +- `live_trading/regime_model.pkl` - Trained ML model +- `logs/live_trading.log` - Trading logs + +## Risk Warning + +This is experimental trading software. Use at your own risk: +- Always start with demo trading +- Never risk more than you can afford to lose +- Monitor the bot regularly +- Have a kill switch ready (Ctrl+C) + +## Troubleshooting + +### API Key Issues +- Ensure API keys have trading permissions +- For demo trading, use demo-specific API keys +- Check that passphrase matches exactly + +### No Signals Generated +- The strategy requires the ML model to be trained +- Need at least 200 candles of data +- Model trains automatically on first run + +### Position Sync Issues +- The bot syncs with exchange positions on each cycle +- If positions are closed manually, the bot will detect this diff --git a/live_trading/__init__.py b/live_trading/__init__.py new file mode 100644 index 0000000..428d538 --- /dev/null +++ b/live_trading/__init__.py @@ -0,0 +1,6 @@ +""" +Live Trading Module for Regime Reversion Strategy on OKX. + +This module implements live trading using the ML-based regime detection +and mean reversion strategy on OKX perpetual futures. +""" diff --git a/live_trading/config.py b/live_trading/config.py new file mode 100644 index 0000000..22fbe61 --- /dev/null +++ b/live_trading/config.py @@ -0,0 +1,120 @@ +""" +Configuration for Live Trading. + +Loads OKX API credentials from environment variables. +Uses demo/sandbox mode by default for paper trading. +""" +import os +from pathlib import Path +from dataclasses import dataclass, field +from dotenv import load_dotenv + +# Load .env from sibling project (BTC_spot_MVRV) +ENV_PATH = Path(__file__).parent.parent.parent / "BTC_spot_MVRV" / ".env" +if ENV_PATH.exists(): + load_dotenv(ENV_PATH) +else: + # Fallback to local .env + load_dotenv() + + +@dataclass +class OKXConfig: + """OKX API configuration.""" + api_key: str = field(default_factory=lambda: "") + secret: str = field(default_factory=lambda: "") + password: str = field(default_factory=lambda: "") + demo_mode: bool = field(default_factory=lambda: True) + + def __post_init__(self): + """Load credentials based on demo mode setting.""" + # Check demo mode first + self.demo_mode = os.getenv("OKX_DEMO_MODE", "true").lower() in ("true", "1", "yes") + + if self.demo_mode: + # Load demo-specific credentials if available + self.api_key = os.getenv("OKX_DEMO_API_KEY", os.getenv("OKX_API_KEY", "")) + self.secret = os.getenv("OKX_DEMO_SECRET", os.getenv("OKX_SECRET", "")) + self.password = os.getenv("OKX_DEMO_PASSWORD", os.getenv("OKX_PASSWORD", "")) + else: + # Load live credentials + self.api_key = os.getenv("OKX_API_KEY", "") + self.secret = os.getenv("OKX_SECRET", "") + self.password = os.getenv("OKX_PASSWORD", "") + + def validate(self) -> None: + """Validate that required credentials are present.""" + mode = "demo" if self.demo_mode else "live" + if not self.api_key: + raise ValueError(f"OKX API key not set for {mode} mode") + if not self.secret: + raise ValueError(f"OKX secret not set for {mode} mode") + if not self.password: + raise ValueError(f"OKX password not set for {mode} mode") + + +@dataclass +class TradingConfig: + """Trading parameters configuration.""" + # Trading pairs + eth_symbol: str = "ETH/USDT:USDT" # ETH perpetual (primary trading asset) + btc_symbol: str = "BTC/USDT:USDT" # BTC perpetual (context asset) + + # Timeframe + timeframe: str = "1h" + candles_to_fetch: int = 500 # Enough for feature calculation + + # Position sizing + max_position_usdt: float = 1000.0 # Max position size in USDT + min_position_usdt: float = 10.0 # Min position size in USDT + leverage: int = 1 # Leverage (1x = no leverage) + margin_mode: str = "cross" # "cross" or "isolated" + + # Risk management + stop_loss_pct: float = 0.06 # 6% stop loss + take_profit_pct: float = 0.05 # 5% take profit + max_concurrent_positions: int = 1 # Max open positions + + # Strategy parameters (from regime_strategy.py) + z_entry_threshold: float = 1.0 # Enter when |Z| > 1.0 + z_window: int = 24 # 24h rolling Z-score window + model_prob_threshold: float = 0.5 # ML model probability threshold + funding_threshold: float = 0.005 # Funding rate filter threshold + + # Execution + sleep_seconds: int = 3600 # Run every hour (1h candles) + slippage_pct: float = 0.001 # 0.1% slippage buffer + + +@dataclass +class PathConfig: + """File paths configuration.""" + base_dir: Path = field( + default_factory=lambda: Path(__file__).parent.parent + ) + data_dir: Path = field(default=None) + logs_dir: Path = field(default=None) + model_path: Path = field(default=None) + positions_file: Path = field(default=None) + trade_log_file: Path = field(default=None) + cq_data_path: Path = field(default=None) + + def __post_init__(self): + self.data_dir = self.base_dir / "data" + self.logs_dir = self.base_dir / "logs" + self.model_path = self.base_dir / "live_trading" / "regime_model.pkl" + self.positions_file = self.base_dir / "live_trading" / "positions.json" + self.trade_log_file = self.base_dir / "live_trading" / "trade_log.csv" + self.cq_data_path = self.data_dir / "cq_training_data.csv" + + # Ensure directories exist + self.data_dir.mkdir(parents=True, exist_ok=True) + self.logs_dir.mkdir(parents=True, exist_ok=True) + + +def get_config(): + """Get all configuration objects.""" + okx = OKXConfig() + trading = TradingConfig() + paths = PathConfig() + return okx, trading, paths diff --git a/live_trading/data_feed.py b/live_trading/data_feed.py new file mode 100644 index 0000000..4caf711 --- /dev/null +++ b/live_trading/data_feed.py @@ -0,0 +1,216 @@ +""" +Data Feed for Live Trading. + +Fetches real-time OHLCV data from OKX and prepares features +for the regime strategy. +""" +import logging +from datetime import datetime, timezone +from typing import Optional + +import pandas as pd +import numpy as np +import ta + +from .okx_client import OKXClient +from .config import TradingConfig, PathConfig + +logger = logging.getLogger(__name__) + + +class DataFeed: + """ + Real-time data feed for the regime strategy. + + Fetches BTC and ETH OHLCV data from OKX and calculates + the spread-based features required by the ML model. + """ + + def __init__( + self, + okx_client: OKXClient, + trading_config: TradingConfig, + path_config: PathConfig + ): + self.client = okx_client + self.config = trading_config + self.paths = path_config + self.cq_data: Optional[pd.DataFrame] = None + self._load_cq_data() + + def _load_cq_data(self) -> None: + """Load CryptoQuant on-chain data if available.""" + try: + if self.paths.cq_data_path.exists(): + self.cq_data = pd.read_csv( + self.paths.cq_data_path, + index_col='timestamp', + parse_dates=True + ) + if self.cq_data.index.tz is None: + self.cq_data.index = self.cq_data.index.tz_localize('UTC') + logger.info(f"Loaded CryptoQuant data: {len(self.cq_data)} rows") + except Exception as e: + logger.warning(f"Could not load CryptoQuant data: {e}") + self.cq_data = None + + def fetch_ohlcv_data(self) -> tuple[pd.DataFrame, pd.DataFrame]: + """ + Fetch OHLCV data for BTC and ETH. + + Returns: + Tuple of (btc_df, eth_df) DataFrames + """ + # Fetch BTC data + btc_ohlcv = self.client.fetch_ohlcv( + self.config.btc_symbol, + self.config.timeframe, + self.config.candles_to_fetch + ) + btc_df = self._ohlcv_to_dataframe(btc_ohlcv) + + # Fetch ETH data + eth_ohlcv = self.client.fetch_ohlcv( + self.config.eth_symbol, + self.config.timeframe, + self.config.candles_to_fetch + ) + eth_df = self._ohlcv_to_dataframe(eth_ohlcv) + + logger.info( + f"Fetched {len(btc_df)} BTC candles and {len(eth_df)} ETH candles" + ) + + return btc_df, eth_df + + def _ohlcv_to_dataframe(self, ohlcv: list) -> pd.DataFrame: + """Convert OHLCV list to DataFrame.""" + df = pd.DataFrame( + ohlcv, + columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'] + ) + df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True) + df.set_index('timestamp', inplace=True) + return df + + def calculate_features( + self, + btc_df: pd.DataFrame, + eth_df: pd.DataFrame + ) -> pd.DataFrame: + """ + Calculate spread-based features for the regime strategy. + + Args: + btc_df: BTC OHLCV DataFrame + eth_df: ETH OHLCV DataFrame + + Returns: + DataFrame with calculated features + """ + # Align indices + common_idx = btc_df.index.intersection(eth_df.index) + df_btc = btc_df.loc[common_idx].copy() + df_eth = eth_df.loc[common_idx].copy() + + # Calculate spread (ETH/BTC ratio) + spread = df_eth['close'] / df_btc['close'] + + # Z-Score of spread + z_window = self.config.z_window + rolling_mean = spread.rolling(window=z_window).mean() + rolling_std = spread.rolling(window=z_window).std() + z_score = (spread - rolling_mean) / rolling_std + + # Spread technicals + spread_rsi = ta.momentum.RSIIndicator(spread, window=14).rsi() + spread_roc = spread.pct_change(periods=5) * 100 + spread_change_1h = spread.pct_change(periods=1) + + # Volume ratio + vol_ratio = df_eth['volume'] / df_btc['volume'] + vol_ratio_ma = vol_ratio.rolling(window=12).mean() + + # Volatility + ret_btc = df_btc['close'].pct_change() + ret_eth = df_eth['close'].pct_change() + vol_btc = ret_btc.rolling(window=z_window).std() + vol_eth = ret_eth.rolling(window=z_window).std() + vol_spread_ratio = vol_eth / vol_btc + + # Build features DataFrame + features = pd.DataFrame(index=spread.index) + features['spread'] = spread + features['z_score'] = z_score + features['spread_rsi'] = spread_rsi + features['spread_roc'] = spread_roc + features['spread_change_1h'] = spread_change_1h + features['vol_ratio'] = vol_ratio + features['vol_ratio_rel'] = vol_ratio / vol_ratio_ma + features['vol_diff_ratio'] = vol_spread_ratio + + # Add price data for reference + features['btc_close'] = df_btc['close'] + features['eth_close'] = df_eth['close'] + features['eth_volume'] = df_eth['volume'] + + # Merge CryptoQuant data if available + if self.cq_data is not None: + cq_aligned = self.cq_data.reindex(features.index, method='ffill') + + # Calculate derived features + if 'btc_funding' in cq_aligned.columns and 'eth_funding' in cq_aligned.columns: + cq_aligned['funding_diff'] = ( + cq_aligned['eth_funding'] - cq_aligned['btc_funding'] + ) + if 'btc_inflow' in cq_aligned.columns and 'eth_inflow' in cq_aligned.columns: + cq_aligned['inflow_ratio'] = ( + cq_aligned['eth_inflow'] / (cq_aligned['btc_inflow'] + 1) + ) + + features = features.join(cq_aligned) + + return features.dropna() + + def get_latest_data(self) -> Optional[pd.DataFrame]: + """ + Fetch and process latest market data. + + Returns: + DataFrame with features or None on error + """ + try: + btc_df, eth_df = self.fetch_ohlcv_data() + features = self.calculate_features(btc_df, eth_df) + + if features.empty: + logger.warning("No valid features calculated") + return None + + logger.info( + f"Latest data: ETH={features['eth_close'].iloc[-1]:.2f}, " + f"BTC={features['btc_close'].iloc[-1]:.2f}, " + f"Z-Score={features['z_score'].iloc[-1]:.3f}" + ) + + return features + + except Exception as e: + logger.error(f"Error fetching market data: {e}", exc_info=True) + return None + + def get_current_funding_rates(self) -> dict: + """ + Get current funding rates for BTC and ETH. + + Returns: + Dictionary with 'btc_funding' and 'eth_funding' rates + """ + btc_funding = self.client.get_funding_rate(self.config.btc_symbol) + eth_funding = self.client.get_funding_rate(self.config.eth_symbol) + + return { + 'btc_funding': btc_funding, + 'eth_funding': eth_funding, + 'funding_diff': eth_funding - btc_funding, + } diff --git a/live_trading/env.template b/live_trading/env.template new file mode 100644 index 0000000..6642684 --- /dev/null +++ b/live_trading/env.template @@ -0,0 +1,15 @@ +# OKX API Credentials Template +# Copy this file to .env and fill in your credentials +# For demo trading, use your OKX demo account API keys +# Generate keys at: https://www.okx.com/account/my-api (Demo Trading section) + +OKX_API_KEY=your_api_key_here +OKX_SECRET=your_secret_key_here +OKX_PASSWORD=your_passphrase_here + +# Demo Mode: Set to "true" for paper trading (sandbox) +# Set to "false" for live trading with real funds +OKX_DEMO_MODE=true + +# CryptoQuant API (optional, for on-chain features) +CRYPTOQUANT_API_KEY=your_cryptoquant_api_key_here diff --git a/live_trading/live_regime_strategy.py b/live_trading/live_regime_strategy.py new file mode 100644 index 0000000..4ebc4ac --- /dev/null +++ b/live_trading/live_regime_strategy.py @@ -0,0 +1,284 @@ +""" +Live Regime Reversion Strategy. + +Adapts the backtest regime strategy for live trading. +Uses a pre-trained ML model or trains on historical data. +""" +import logging +import pickle +from pathlib import Path +from typing import Optional + +import numpy as np +import pandas as pd +from sklearn.ensemble import RandomForestClassifier + +from .config import TradingConfig, PathConfig + +logger = logging.getLogger(__name__) + + +class LiveRegimeStrategy: + """ + Live trading implementation of the ML-based regime detection + and mean reversion strategy. + + Logic: + 1. Calculates BTC/ETH spread Z-Score + 2. Uses Random Forest to predict reversion probability + 3. Applies funding rate filter + 4. Generates long/short signals on ETH perpetual + """ + + def __init__( + self, + trading_config: TradingConfig, + path_config: PathConfig + ): + self.config = trading_config + self.paths = path_config + self.model: Optional[RandomForestClassifier] = None + self.feature_cols: Optional[list] = None + self._load_or_train_model() + + def _load_or_train_model(self) -> None: + """Load pre-trained model or train a new one.""" + if self.paths.model_path.exists(): + try: + with open(self.paths.model_path, 'rb') as f: + saved = pickle.load(f) + self.model = saved['model'] + self.feature_cols = saved['feature_cols'] + logger.info(f"Loaded model from {self.paths.model_path}") + return + except Exception as e: + logger.warning(f"Could not load model: {e}") + + logger.info("No pre-trained model found. Will train on first data batch.") + + def save_model(self) -> None: + """Save trained model to file.""" + if self.model is None: + return + + try: + with open(self.paths.model_path, 'wb') as f: + pickle.dump({ + 'model': self.model, + 'feature_cols': self.feature_cols, + }, f) + logger.info(f"Saved model to {self.paths.model_path}") + except Exception as e: + logger.error(f"Could not save model: {e}") + + def train_model(self, features: pd.DataFrame) -> None: + """ + Train the Random Forest model on historical data. + + Args: + features: DataFrame with calculated features + """ + logger.info(f"Training model on {len(features)} samples...") + + z_thresh = self.config.z_entry_threshold + horizon = 102 # Optimal horizon from research + profit_target = 0.005 # 0.5% profit threshold + + # Define targets + future_min = features['spread'].rolling(window=horizon).min().shift(-horizon) + future_max = features['spread'].rolling(window=horizon).max().shift(-horizon) + + target_short = features['spread'] * (1 - profit_target) + target_long = features['spread'] * (1 + profit_target) + + success_short = (features['z_score'] > z_thresh) & (future_min < target_short) + success_long = (features['z_score'] < -z_thresh) & (future_max > target_long) + + targets = np.select([success_short, success_long], [1, 1], default=0) + + # Exclude non-feature columns + exclude = ['spread', 'btc_close', 'eth_close', 'eth_volume'] + self.feature_cols = [c for c in features.columns if c not in exclude] + + # Clean features + X = features[self.feature_cols].fillna(0) + X = X.replace([np.inf, -np.inf], 0) + + # Remove rows with invalid targets + valid_mask = ~np.isnan(targets) & future_min.notna().values & future_max.notna().values + X_clean = X[valid_mask] + y_clean = targets[valid_mask] + + if len(X_clean) < 100: + logger.warning("Not enough data to train model") + return + + # Train model + self.model = RandomForestClassifier( + n_estimators=300, + max_depth=5, + min_samples_leaf=30, + class_weight={0: 1, 1: 3}, + random_state=42 + ) + self.model.fit(X_clean, y_clean) + + logger.info(f"Model trained on {len(X_clean)} samples") + self.save_model() + + def generate_signal( + self, + features: pd.DataFrame, + current_funding: dict + ) -> dict: + """ + Generate trading signal from latest features. + + Args: + features: DataFrame with calculated features + current_funding: Dictionary with funding rate data + + Returns: + Signal dictionary with action, side, confidence, etc. + """ + if self.model is None: + # Train model if not available + if len(features) >= 200: + self.train_model(features) + else: + return {'action': 'hold', 'reason': 'model_not_trained'} + + if self.model is None: + return {'action': 'hold', 'reason': 'insufficient_data_for_training'} + + # Get latest row + latest = features.iloc[-1] + z_score = latest['z_score'] + eth_price = latest['eth_close'] + btc_price = latest['btc_close'] + + # Prepare features for prediction + X = features[self.feature_cols].iloc[[-1]].fillna(0) + X = X.replace([np.inf, -np.inf], 0) + + # Get prediction probability + prob = self.model.predict_proba(X)[0, 1] + + # Apply thresholds + z_thresh = self.config.z_entry_threshold + prob_thresh = self.config.model_prob_threshold + + # Determine signal direction + signal = { + 'action': 'hold', + 'side': None, + 'probability': prob, + 'z_score': z_score, + 'eth_price': eth_price, + 'btc_price': btc_price, + 'reason': '', + } + + # Check for entry conditions + if prob > prob_thresh: + if z_score > z_thresh: + # Spread high (ETH expensive relative to BTC) -> Short ETH + signal['action'] = 'entry' + signal['side'] = 'short' + signal['reason'] = f'z_score={z_score:.2f}>threshold, prob={prob:.2f}' + elif z_score < -z_thresh: + # Spread low (ETH cheap relative to BTC) -> Long ETH + signal['action'] = 'entry' + signal['side'] = 'long' + signal['reason'] = f'z_score={z_score:.2f}<-threshold, prob={prob:.2f}' + else: + signal['reason'] = f'z_score={z_score:.2f} within threshold' + else: + signal['reason'] = f'prob={prob:.2f} funding_thresh: + # High positive funding = overheated, don't go long + signal['action'] = 'hold' + signal['reason'] = f'funding_filter_blocked_long (funding={btc_funding:.4f})' + elif signal['side'] == 'short' and btc_funding < -funding_thresh: + # High negative funding = oversold, don't go short + signal['action'] = 'hold' + signal['reason'] = f'funding_filter_blocked_short (funding={btc_funding:.4f})' + + # Check for exit conditions (mean reversion complete) + if signal['action'] == 'hold': + # Z-score crossed back through 0 + if abs(z_score) < 0.3: + signal['action'] = 'check_exit' + signal['reason'] = f'z_score_reverted_to_mean ({z_score:.2f})' + + logger.info( + f"Signal: {signal['action']} {signal['side'] or ''} " + f"(prob={prob:.2f}, z={z_score:.2f}, reason={signal['reason']})" + ) + + return signal + + def calculate_position_size( + self, + signal: dict, + available_usdt: float + ) -> float: + """ + Calculate position size based on signal confidence. + + Args: + signal: Signal dictionary with probability + available_usdt: Available USDT balance + + Returns: + Position size in USDT + """ + prob = signal.get('probability', 0.5) + + # Base size is max_position_usdt + base_size = min(available_usdt, self.config.max_position_usdt) + + # Scale by probability (1.0x at 0.5 prob, up to 1.6x at 0.8 prob) + scale = 1.0 + (prob - 0.5) * 2.0 + scale = max(1.0, min(scale, 2.0)) # Clamp between 1x and 2x + + size = base_size * scale + + # Ensure minimum position size + if size < self.config.min_position_usdt: + return 0.0 + + return min(size, available_usdt * 0.95) # Leave 5% buffer + + def calculate_sl_tp( + self, + entry_price: float, + side: str + ) -> tuple[float, float]: + """ + Calculate stop-loss and take-profit prices. + + Args: + entry_price: Entry price + side: "long" or "short" + + Returns: + Tuple of (stop_loss_price, take_profit_price) + """ + sl_pct = self.config.stop_loss_pct + tp_pct = self.config.take_profit_pct + + if side == "long": + stop_loss = entry_price * (1 - sl_pct) + take_profit = entry_price * (1 + tp_pct) + else: # short + stop_loss = entry_price * (1 + sl_pct) + take_profit = entry_price * (1 - tp_pct) + + return stop_loss, take_profit diff --git a/live_trading/main.py b/live_trading/main.py new file mode 100644 index 0000000..16d8514 --- /dev/null +++ b/live_trading/main.py @@ -0,0 +1,390 @@ +#!/usr/bin/env python3 +""" +Live Trading Bot for Regime Reversion Strategy on OKX. + +This script runs the regime-based mean reversion strategy +on ETH perpetual futures using OKX exchange. + +Usage: + # Run with demo account (default) + uv run python -m live_trading.main + + # Run with specific settings + uv run python -m live_trading.main --max-position 500 --leverage 2 +""" +import argparse +import logging +import signal +import sys +import time +from datetime import datetime, timezone +from pathlib import Path + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from live_trading.config import get_config, OKXConfig, TradingConfig, PathConfig +from live_trading.okx_client import OKXClient +from live_trading.data_feed import DataFeed +from live_trading.position_manager import PositionManager +from live_trading.live_regime_strategy import LiveRegimeStrategy + + +def setup_logging(log_dir: Path) -> logging.Logger: + """Configure logging for the trading bot.""" + log_file = log_dir / "live_trading.log" + + logging.basicConfig( + level=logging.INFO, + format='%(asctime)s [%(levelname)s] %(name)s: %(message)s', + handlers=[ + logging.FileHandler(log_file), + logging.StreamHandler(sys.stdout), + ], + force=True + ) + + return logging.getLogger(__name__) + + +class LiveTradingBot: + """ + Main trading bot orchestrator. + + Coordinates data fetching, signal generation, and order execution + in a continuous loop. + """ + + def __init__( + self, + okx_config: OKXConfig, + trading_config: TradingConfig, + path_config: PathConfig + ): + self.okx_config = okx_config + self.trading_config = trading_config + self.path_config = path_config + + self.logger = logging.getLogger(__name__) + self.running = True + + # Initialize components + self.logger.info("Initializing trading bot components...") + + self.okx_client = OKXClient(okx_config, trading_config) + self.data_feed = DataFeed(self.okx_client, trading_config, path_config) + self.position_manager = PositionManager( + self.okx_client, trading_config, path_config + ) + self.strategy = LiveRegimeStrategy(trading_config, path_config) + + # Register signal handlers for graceful shutdown + signal.signal(signal.SIGINT, self._handle_shutdown) + signal.signal(signal.SIGTERM, self._handle_shutdown) + + self._print_startup_banner() + + def _print_startup_banner(self) -> None: + """Print startup information.""" + mode = "DEMO/SANDBOX" if self.okx_config.demo_mode else "LIVE" + + print("=" * 60) + print(f" Regime Reversion Strategy - Live Trading Bot") + print("=" * 60) + print(f" Mode: {mode}") + print(f" Trading Pair: {self.trading_config.eth_symbol}") + print(f" Context Pair: {self.trading_config.btc_symbol}") + print(f" Timeframe: {self.trading_config.timeframe}") + print(f" Max Position: ${self.trading_config.max_position_usdt}") + print(f" Leverage: {self.trading_config.leverage}x") + print(f" Stop Loss: {self.trading_config.stop_loss_pct * 100:.1f}%") + print(f" Take Profit: {self.trading_config.take_profit_pct * 100:.1f}%") + print(f" Cycle Interval: {self.trading_config.sleep_seconds // 60} minutes") + print("=" * 60) + + if not self.okx_config.demo_mode: + print("\n *** WARNING: LIVE TRADING MODE - REAL FUNDS AT RISK ***\n") + + def _handle_shutdown(self, signum, frame) -> None: + """Handle shutdown signals gracefully.""" + self.logger.info("Shutdown signal received, stopping...") + self.running = False + + def run_trading_cycle(self) -> None: + """ + Execute one trading cycle. + + 1. Fetch latest market data + 2. Update open positions + 3. Generate trading signal + 4. Execute trades if signal triggers + """ + cycle_start = datetime.now(timezone.utc) + self.logger.info(f"--- Trading Cycle Start: {cycle_start.isoformat()} ---") + + try: + # 1. Fetch market data + features = self.data_feed.get_latest_data() + if features is None or features.empty: + self.logger.warning("No market data available, skipping cycle") + return + + # Get current prices + eth_price = features['eth_close'].iloc[-1] + btc_price = features['btc_close'].iloc[-1] + + current_prices = { + self.trading_config.eth_symbol: eth_price, + self.trading_config.btc_symbol: btc_price, + } + + # 2. Update existing positions (check SL/TP) + closed_trades = self.position_manager.update_positions(current_prices) + if closed_trades: + for trade in closed_trades: + self.logger.info( + f"Trade closed: {trade['trade_id']} " + f"PnL=${trade['pnl_usd']:.2f} ({trade['reason']})" + ) + + # 3. Sync with exchange positions + self.position_manager.sync_with_exchange() + + # 4. Get current funding rates + funding = self.data_feed.get_current_funding_rates() + + # 5. Generate trading signal + signal = self.strategy.generate_signal(features, funding) + + # 6. Execute trades based on signal + if signal['action'] == 'entry': + self._execute_entry(signal, eth_price) + elif signal['action'] == 'check_exit': + self._execute_exit(signal) + + # 7. Log portfolio summary + summary = self.position_manager.get_portfolio_summary() + self.logger.info( + f"Portfolio: {summary['open_positions']} positions, " + f"exposure=${summary['total_exposure_usdt']:.2f}, " + f"unrealized_pnl=${summary['total_unrealized_pnl']:.2f}" + ) + + except Exception as e: + self.logger.error(f"Trading cycle error: {e}", exc_info=True) + # Save positions on error + self.position_manager.save_positions() + + cycle_duration = (datetime.now(timezone.utc) - cycle_start).total_seconds() + self.logger.info(f"--- Cycle completed in {cycle_duration:.1f}s ---") + + def _execute_entry(self, signal: dict, current_price: float) -> None: + """Execute entry trade.""" + symbol = self.trading_config.eth_symbol + side = signal['side'] + + # Check if we can open a position + if not self.position_manager.can_open_position(): + self.logger.info("Cannot open position: max positions reached") + return + + # Get account balance + balance = self.okx_client.get_balance() + available_usdt = balance['free'] + + # Calculate position size + size_usdt = self.strategy.calculate_position_size(signal, available_usdt) + if size_usdt <= 0: + self.logger.info("Position size too small, skipping entry") + return + + size_eth = size_usdt / current_price + + # Calculate SL/TP + stop_loss, take_profit = self.strategy.calculate_sl_tp(current_price, side) + + self.logger.info( + f"Executing {side.upper()} entry: {size_eth:.4f} ETH @ {current_price:.2f} " + f"(${size_usdt:.2f}), SL={stop_loss:.2f}, TP={take_profit:.2f}" + ) + + try: + # Place market order + order_side = "buy" if side == "long" else "sell" + order = self.okx_client.place_market_order(symbol, order_side, size_eth) + + # Get filled price (handle None values from OKX response) + filled_price = order.get('average') or order.get('price') or current_price + filled_amount = order.get('filled') or order.get('amount') or size_eth + + # Ensure we have valid numeric values + if filled_price is None or filled_price == 0: + self.logger.warning(f"No fill price in order response, using current price: {current_price}") + filled_price = current_price + if filled_amount is None or filled_amount == 0: + self.logger.warning(f"No fill amount in order response, using requested: {size_eth}") + filled_amount = size_eth + + # Recalculate SL/TP with filled price + stop_loss, take_profit = self.strategy.calculate_sl_tp(filled_price, side) + + # Get order ID from response + order_id = order.get('id', '') + + # Record position locally + position = self.position_manager.open_position( + symbol=symbol, + side=side, + entry_price=filled_price, + size=filled_amount, + stop_loss_price=stop_loss, + take_profit_price=take_profit, + order_id=order_id, + ) + + if position: + self.logger.info( + f"Position opened: {position.trade_id}, " + f"{filled_amount:.4f} ETH @ {filled_price:.2f}" + ) + + # Try to set SL/TP on exchange + try: + self.okx_client.set_stop_loss_take_profit( + symbol, side, filled_amount, stop_loss, take_profit + ) + except Exception as e: + self.logger.warning(f"Could not set SL/TP on exchange: {e}") + + except Exception as e: + self.logger.error(f"Order execution failed: {e}", exc_info=True) + + def _execute_exit(self, signal: dict) -> None: + """Execute exit based on mean reversion signal.""" + symbol = self.trading_config.eth_symbol + + # Get position for ETH + position = self.position_manager.get_position_for_symbol(symbol) + if not position: + return + + current_price = signal.get('eth_price', position.current_price) + + self.logger.info( + f"Mean reversion exit signal: closing {position.trade_id} " + f"@ {current_price:.2f}" + ) + + try: + # Close position on exchange + exit_order = self.okx_client.close_position(symbol) + exit_order_id = exit_order.get('id', '') if exit_order else '' + + # Record closure locally + self.position_manager.close_position( + position.trade_id, + current_price, + reason="mean_reversion_complete", + exit_order_id=exit_order_id, + ) + + except Exception as e: + self.logger.error(f"Exit execution failed: {e}", exc_info=True) + + def run(self) -> None: + """Main trading loop.""" + self.logger.info("Starting trading loop...") + + while self.running: + try: + self.run_trading_cycle() + + if self.running: + sleep_seconds = self.trading_config.sleep_seconds + minutes = sleep_seconds // 60 + self.logger.info(f"Sleeping for {minutes} minutes...") + + # Sleep in smaller chunks to allow faster shutdown + for _ in range(sleep_seconds): + if not self.running: + break + time.sleep(1) + + except KeyboardInterrupt: + self.logger.info("Keyboard interrupt received") + break + except Exception as e: + self.logger.error(f"Unexpected error in main loop: {e}", exc_info=True) + time.sleep(60) # Wait before retry + + # Cleanup + self.logger.info("Shutting down...") + self.position_manager.save_positions() + self.logger.info("Shutdown complete") + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description="Live Trading Bot for Regime Reversion Strategy" + ) + parser.add_argument( + "--max-position", + type=float, + default=None, + help="Maximum position size in USDT" + ) + parser.add_argument( + "--leverage", + type=int, + default=None, + help="Trading leverage (1-125)" + ) + parser.add_argument( + "--interval", + type=int, + default=None, + help="Trading cycle interval in seconds" + ) + parser.add_argument( + "--live", + action="store_true", + help="Use live trading mode (requires OKX_DEMO_MODE=false)" + ) + return parser.parse_args() + + +def main(): + """Main entry point.""" + args = parse_args() + + # Load configuration + okx_config, trading_config, path_config = get_config() + + # Apply command line overrides + if args.max_position is not None: + trading_config.max_position_usdt = args.max_position + if args.leverage is not None: + trading_config.leverage = args.leverage + if args.interval is not None: + trading_config.sleep_seconds = args.interval + if args.live: + okx_config.demo_mode = False + + # Setup logging + logger = setup_logging(path_config.logs_dir) + + try: + # Create and run bot + bot = LiveTradingBot(okx_config, trading_config, path_config) + bot.run() + except ValueError as e: + logger.error(f"Configuration error: {e}") + sys.exit(1) + except Exception as e: + logger.error(f"Fatal error: {e}", exc_info=True) + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/live_trading/okx_client.py b/live_trading/okx_client.py new file mode 100644 index 0000000..e0e97e5 --- /dev/null +++ b/live_trading/okx_client.py @@ -0,0 +1,338 @@ +""" +OKX Exchange Client for Live Trading. + +Handles connection to OKX API, order execution, and account management. +Supports demo/sandbox mode for paper trading. +""" +import logging +from typing import Optional +from datetime import datetime, timezone + +import ccxt + +from .config import OKXConfig, TradingConfig + +logger = logging.getLogger(__name__) + + +class OKXClient: + """ + OKX Exchange client wrapper using CCXT. + + Supports both live and demo (sandbox) trading modes. + Demo mode uses OKX's official sandbox environment. + """ + + def __init__(self, okx_config: OKXConfig, trading_config: TradingConfig): + self.okx_config = okx_config + self.trading_config = trading_config + self.exchange: Optional[ccxt.okx] = None + self._setup_exchange() + + def _setup_exchange(self) -> None: + """Initialize CCXT OKX exchange instance.""" + self.okx_config.validate() + + config = { + 'apiKey': self.okx_config.api_key, + 'secret': self.okx_config.secret, + 'password': self.okx_config.password, + 'sandbox': self.okx_config.demo_mode, + 'options': { + 'defaultType': 'swap', # Perpetual futures + }, + 'timeout': 30000, + 'enableRateLimit': True, + } + + self.exchange = ccxt.okx(config) + + mode_str = "DEMO/SANDBOX" if self.okx_config.demo_mode else "LIVE" + logger.info(f"OKX Exchange initialized in {mode_str} mode") + + # Configure trading settings + self._configure_trading_settings() + + def _configure_trading_settings(self) -> None: + """Configure leverage and margin mode.""" + symbol = self.trading_config.eth_symbol + leverage = self.trading_config.leverage + margin_mode = self.trading_config.margin_mode + + try: + # Set position mode to one-way (net) first + self.exchange.set_position_mode(False) # False = one-way mode + logger.info("Position mode set to One-Way (Net)") + except Exception as e: + # Position mode might already be set + logger.debug(f"Position mode setting: {e}") + + try: + # Set margin mode with leverage parameter (required by OKX) + self.exchange.set_margin_mode( + margin_mode, + symbol, + params={'lever': leverage} + ) + logger.info( + f"Margin mode set to {margin_mode} with {leverage}x leverage " + f"for {symbol}" + ) + except Exception as e: + logger.warning(f"Could not set margin mode: {e}") + # Try setting leverage separately + try: + self.exchange.set_leverage(leverage, symbol) + logger.info(f"Leverage set to {leverage}x for {symbol}") + except Exception as e2: + logger.warning(f"Could not set leverage: {e2}") + + def fetch_ohlcv( + self, + symbol: str, + timeframe: str = "1h", + limit: int = 500 + ) -> list: + """ + Fetch OHLCV candle data. + + Args: + symbol: Trading pair symbol (e.g., "ETH/USDT:USDT") + timeframe: Candle timeframe (e.g., "1h") + limit: Number of candles to fetch + + Returns: + List of OHLCV data + """ + return self.exchange.fetch_ohlcv(symbol, timeframe, limit=limit) + + def get_balance(self) -> dict: + """ + Get account balance. + + Returns: + Balance dictionary with 'total' and 'free' USDT amounts + """ + balance = self.exchange.fetch_balance() + return { + 'total': balance.get('USDT', {}).get('total', 0), + 'free': balance.get('USDT', {}).get('free', 0), + } + + def get_positions(self) -> list: + """ + Get open positions. + + Returns: + List of open position dictionaries + """ + positions = self.exchange.fetch_positions() + return [p for p in positions if float(p.get('contracts', 0)) != 0] + + def get_position(self, symbol: str) -> Optional[dict]: + """ + Get position for a specific symbol. + + Args: + symbol: Trading pair symbol + + Returns: + Position dictionary or None if no position + """ + positions = self.get_positions() + for pos in positions: + if pos.get('symbol') == symbol: + return pos + return None + + def place_market_order( + self, + symbol: str, + side: str, + amount: float, + reduce_only: bool = False + ) -> dict: + """ + Place a market order. + + Args: + symbol: Trading pair symbol + side: "buy" or "sell" + amount: Order amount in base currency + reduce_only: If True, only reduce existing position + + Returns: + Order result dictionary + """ + params = { + 'tdMode': self.trading_config.margin_mode, + } + if reduce_only: + params['reduceOnly'] = True + + order = self.exchange.create_market_order( + symbol, side, amount, params=params + ) + logger.info( + f"Market {side.upper()} order placed: {amount} {symbol} " + f"@ market price, order_id={order['id']}" + ) + return order + + def place_limit_order( + self, + symbol: str, + side: str, + amount: float, + price: float, + reduce_only: bool = False + ) -> dict: + """ + Place a limit order. + + Args: + symbol: Trading pair symbol + side: "buy" or "sell" + amount: Order amount in base currency + price: Limit price + reduce_only: If True, only reduce existing position + + Returns: + Order result dictionary + """ + params = { + 'tdMode': self.trading_config.margin_mode, + } + if reduce_only: + params['reduceOnly'] = True + + order = self.exchange.create_limit_order( + symbol, side, amount, price, params=params + ) + logger.info( + f"Limit {side.upper()} order placed: {amount} {symbol} " + f"@ {price}, order_id={order['id']}" + ) + return order + + def set_stop_loss_take_profit( + self, + symbol: str, + side: str, + amount: float, + stop_loss_price: float, + take_profit_price: float + ) -> tuple: + """ + Set stop-loss and take-profit orders. + + Args: + symbol: Trading pair symbol + side: Position side ("long" or "short") + amount: Position size + stop_loss_price: Stop-loss trigger price + take_profit_price: Take-profit trigger price + + Returns: + Tuple of (sl_order, tp_order) + """ + # For long position: SL sells, TP sells + # For short position: SL buys, TP buys + close_side = "sell" if side == "long" else "buy" + + # Stop-loss order + sl_params = { + 'tdMode': self.trading_config.margin_mode, + 'reduceOnly': True, + 'stopLossPrice': stop_loss_price, + } + + try: + sl_order = self.exchange.create_order( + symbol, 'market', close_side, amount, + params={ + 'tdMode': self.trading_config.margin_mode, + 'reduceOnly': True, + 'slTriggerPx': str(stop_loss_price), + 'slOrdPx': '-1', # Market price + } + ) + logger.info(f"Stop-loss set at {stop_loss_price}") + except Exception as e: + logger.warning(f"Could not set stop-loss: {e}") + sl_order = None + + # Take-profit order + try: + tp_order = self.exchange.create_order( + symbol, 'market', close_side, amount, + params={ + 'tdMode': self.trading_config.margin_mode, + 'reduceOnly': True, + 'tpTriggerPx': str(take_profit_price), + 'tpOrdPx': '-1', # Market price + } + ) + logger.info(f"Take-profit set at {take_profit_price}") + except Exception as e: + logger.warning(f"Could not set take-profit: {e}") + tp_order = None + + return sl_order, tp_order + + def close_position(self, symbol: str) -> Optional[dict]: + """ + Close an open position. + + Args: + symbol: Trading pair symbol + + Returns: + Order result or None if no position + """ + position = self.get_position(symbol) + if not position: + logger.info(f"No open position for {symbol}") + return None + + contracts = abs(float(position.get('contracts', 0))) + if contracts == 0: + return None + + side = position.get('side', 'long') + close_side = "sell" if side == "long" else "buy" + + order = self.place_market_order( + symbol, close_side, contracts, reduce_only=True + ) + logger.info(f"Position closed for {symbol}") + return order + + def get_ticker(self, symbol: str) -> dict: + """ + Get current ticker/price for a symbol. + + Args: + symbol: Trading pair symbol + + Returns: + Ticker dictionary with 'last', 'bid', 'ask' prices + """ + return self.exchange.fetch_ticker(symbol) + + def get_funding_rate(self, symbol: str) -> float: + """ + Get current funding rate for a perpetual symbol. + + Args: + symbol: Trading pair symbol + + Returns: + Current funding rate as decimal + """ + try: + funding = self.exchange.fetch_funding_rate(symbol) + return float(funding.get('fundingRate', 0)) + except Exception as e: + logger.warning(f"Could not fetch funding rate: {e}") + return 0.0 diff --git a/live_trading/position_manager.py b/live_trading/position_manager.py new file mode 100644 index 0000000..8c079c8 --- /dev/null +++ b/live_trading/position_manager.py @@ -0,0 +1,369 @@ +""" +Position Manager for Live Trading. + +Tracks open positions, manages risk, and handles SL/TP logic. +""" +import json +import logging +from datetime import datetime, timezone +from pathlib import Path +from typing import Optional +from dataclasses import dataclass, field, asdict + +from .okx_client import OKXClient +from .config import TradingConfig, PathConfig + +logger = logging.getLogger(__name__) + + +@dataclass +class Position: + """Represents an open trading position.""" + trade_id: str + symbol: str + side: str # "long" or "short" + entry_price: float + entry_time: str # ISO format + size: float # Amount in base currency (e.g., ETH) + size_usdt: float # Notional value in USDT + stop_loss_price: float + take_profit_price: float + current_price: float = 0.0 + unrealized_pnl: float = 0.0 + unrealized_pnl_pct: float = 0.0 + order_id: str = "" # Entry order ID from exchange + + def update_pnl(self, current_price: float) -> None: + """Update unrealized PnL based on current price.""" + self.current_price = current_price + + if self.side == "long": + self.unrealized_pnl = (current_price - self.entry_price) * self.size + self.unrealized_pnl_pct = (current_price / self.entry_price - 1) * 100 + else: # short + self.unrealized_pnl = (self.entry_price - current_price) * self.size + self.unrealized_pnl_pct = (1 - current_price / self.entry_price) * 100 + + def should_stop_loss(self, current_price: float) -> bool: + """Check if stop-loss should trigger.""" + if self.side == "long": + return current_price <= self.stop_loss_price + return current_price >= self.stop_loss_price + + def should_take_profit(self, current_price: float) -> bool: + """Check if take-profit should trigger.""" + if self.side == "long": + return current_price >= self.take_profit_price + return current_price <= self.take_profit_price + + def to_dict(self) -> dict: + """Convert to dictionary for JSON serialization.""" + return asdict(self) + + @classmethod + def from_dict(cls, data: dict) -> 'Position': + """Create Position from dictionary.""" + return cls(**data) + + +class PositionManager: + """ + Manages trading positions with persistence. + + Tracks open positions, enforces risk limits, and handles + position lifecycle (open, update, close). + """ + + def __init__( + self, + okx_client: OKXClient, + trading_config: TradingConfig, + path_config: PathConfig + ): + self.client = okx_client + self.config = trading_config + self.paths = path_config + self.positions: dict[str, Position] = {} + self.trade_log: list[dict] = [] + self._load_positions() + + def _load_positions(self) -> None: + """Load positions from file.""" + if self.paths.positions_file.exists(): + try: + with open(self.paths.positions_file, 'r') as f: + data = json.load(f) + for trade_id, pos_data in data.items(): + self.positions[trade_id] = Position.from_dict(pos_data) + logger.info(f"Loaded {len(self.positions)} positions from file") + except Exception as e: + logger.warning(f"Could not load positions: {e}") + + def save_positions(self) -> None: + """Save positions to file.""" + try: + data = { + trade_id: pos.to_dict() + for trade_id, pos in self.positions.items() + } + with open(self.paths.positions_file, 'w') as f: + json.dump(data, f, indent=2) + logger.debug(f"Saved {len(self.positions)} positions") + except Exception as e: + logger.error(f"Could not save positions: {e}") + + def can_open_position(self) -> bool: + """Check if we can open a new position.""" + return len(self.positions) < self.config.max_concurrent_positions + + def get_position_for_symbol(self, symbol: str) -> Optional[Position]: + """Get position for a specific symbol.""" + for pos in self.positions.values(): + if pos.symbol == symbol: + return pos + return None + + def open_position( + self, + symbol: str, + side: str, + entry_price: float, + size: float, + stop_loss_price: float, + take_profit_price: float, + order_id: str = "" + ) -> Optional[Position]: + """ + Open a new position. + + Args: + symbol: Trading pair symbol + side: "long" or "short" + entry_price: Entry price + size: Position size in base currency + stop_loss_price: Stop-loss price + take_profit_price: Take-profit price + order_id: Entry order ID from exchange + + Returns: + Position object or None if failed + """ + if not self.can_open_position(): + logger.warning("Cannot open position: max concurrent positions reached") + return None + + # Check if already have position for this symbol + existing = self.get_position_for_symbol(symbol) + if existing: + logger.warning(f"Already have position for {symbol}") + return None + + # Generate trade ID + now = datetime.now(timezone.utc) + trade_id = f"{symbol}_{now.strftime('%Y%m%d_%H%M%S')}" + + position = Position( + trade_id=trade_id, + symbol=symbol, + side=side, + entry_price=entry_price, + entry_time=now.isoformat(), + size=size, + size_usdt=entry_price * size, + stop_loss_price=stop_loss_price, + take_profit_price=take_profit_price, + current_price=entry_price, + order_id=order_id, + ) + + self.positions[trade_id] = position + self.save_positions() + + logger.info( + f"Opened {side.upper()} position: {size} {symbol} @ {entry_price}, " + f"SL={stop_loss_price}, TP={take_profit_price}" + ) + + return position + + def close_position( + self, + trade_id: str, + exit_price: float, + reason: str = "manual", + exit_order_id: str = "" + ) -> Optional[dict]: + """ + Close a position and record the trade. + + Args: + trade_id: Position trade ID + exit_price: Exit price + reason: Reason for closing (e.g., "stop_loss", "take_profit", "signal") + exit_order_id: Exit order ID from exchange + + Returns: + Trade record dictionary + """ + if trade_id not in self.positions: + logger.warning(f"Position {trade_id} not found") + return None + + position = self.positions[trade_id] + position.update_pnl(exit_price) + + # Calculate final PnL + entry_time = datetime.fromisoformat(position.entry_time) + exit_time = datetime.now(timezone.utc) + hold_duration = (exit_time - entry_time).total_seconds() / 3600 # hours + + trade_record = { + 'trade_id': trade_id, + 'symbol': position.symbol, + 'side': position.side, + 'entry_price': position.entry_price, + 'exit_price': exit_price, + 'size': position.size, + 'size_usdt': position.size_usdt, + 'pnl_usd': position.unrealized_pnl, + 'pnl_pct': position.unrealized_pnl_pct, + 'entry_time': position.entry_time, + 'exit_time': exit_time.isoformat(), + 'hold_duration_hours': hold_duration, + 'reason': reason, + 'order_id_entry': position.order_id, + 'order_id_exit': exit_order_id, + } + + self.trade_log.append(trade_record) + del self.positions[trade_id] + self.save_positions() + self._append_trade_log(trade_record) + + logger.info( + f"Closed {position.side.upper()} position: {position.size} {position.symbol} " + f"@ {exit_price}, PnL=${position.unrealized_pnl:.2f} ({position.unrealized_pnl_pct:.2f}%), " + f"reason={reason}" + ) + + return trade_record + + def _append_trade_log(self, trade_record: dict) -> None: + """Append trade record to CSV log file.""" + import csv + + file_exists = self.paths.trade_log_file.exists() + + with open(self.paths.trade_log_file, 'a', newline='') as f: + writer = csv.DictWriter(f, fieldnames=trade_record.keys()) + if not file_exists: + writer.writeheader() + writer.writerow(trade_record) + + def update_positions(self, current_prices: dict[str, float]) -> list[dict]: + """ + Update all positions with current prices and check SL/TP. + + Args: + current_prices: Dictionary of symbol -> current price + + Returns: + List of closed trade records + """ + closed_trades = [] + + for trade_id in list(self.positions.keys()): + position = self.positions[trade_id] + + if position.symbol not in current_prices: + continue + + current_price = current_prices[position.symbol] + position.update_pnl(current_price) + + # Check stop-loss + if position.should_stop_loss(current_price): + logger.warning( + f"Stop-loss triggered for {trade_id} at {current_price}" + ) + # Close position on exchange + exit_order_id = "" + try: + exit_order = self.client.close_position(position.symbol) + exit_order_id = exit_order.get('id', '') if exit_order else '' + except Exception as e: + logger.error(f"Failed to close position on exchange: {e}") + + record = self.close_position(trade_id, current_price, "stop_loss", exit_order_id) + if record: + closed_trades.append(record) + continue + + # Check take-profit + if position.should_take_profit(current_price): + logger.info( + f"Take-profit triggered for {trade_id} at {current_price}" + ) + # Close position on exchange + exit_order_id = "" + try: + exit_order = self.client.close_position(position.symbol) + exit_order_id = exit_order.get('id', '') if exit_order else '' + except Exception as e: + logger.error(f"Failed to close position on exchange: {e}") + + record = self.close_position(trade_id, current_price, "take_profit", exit_order_id) + if record: + closed_trades.append(record) + + self.save_positions() + return closed_trades + + def sync_with_exchange(self) -> None: + """ + Sync local positions with exchange positions. + + Reconciles any discrepancies between local tracking + and actual exchange positions. + """ + try: + exchange_positions = self.client.get_positions() + exchange_symbols = {p['symbol'] for p in exchange_positions} + + # Check for positions we have locally but not on exchange + for trade_id in list(self.positions.keys()): + pos = self.positions[trade_id] + if pos.symbol not in exchange_symbols: + logger.warning( + f"Position {trade_id} not found on exchange, removing" + ) + # Get last price and close + try: + ticker = self.client.get_ticker(pos.symbol) + exit_price = ticker['last'] + except Exception: + exit_price = pos.current_price + + self.close_position(trade_id, exit_price, "sync_removed") + + logger.info(f"Position sync complete: {len(self.positions)} local positions") + + except Exception as e: + logger.error(f"Position sync failed: {e}") + + def get_portfolio_summary(self) -> dict: + """ + Get portfolio summary. + + Returns: + Dictionary with portfolio statistics + """ + total_exposure = sum(p.size_usdt for p in self.positions.values()) + total_unrealized_pnl = sum(p.unrealized_pnl for p in self.positions.values()) + + return { + 'open_positions': len(self.positions), + 'total_exposure_usdt': total_exposure, + 'total_unrealized_pnl': total_unrealized_pnl, + 'positions': [p.to_dict() for p in self.positions.values()], + } diff --git a/pyproject.toml b/pyproject.toml index 8787753..b4775d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,10 @@ dependencies = [ "plotly>=5.24.0", "requests>=2.32.5", "python-dotenv>=1.2.1", + # API dependencies + "fastapi>=0.115.0", + "uvicorn[standard]>=0.34.0", + "sqlalchemy>=2.0.0", ] [project.optional-dependencies] diff --git a/tasks/prd-interactive-ui.md b/tasks/prd-interactive-ui.md new file mode 100644 index 0000000..5e07b2e --- /dev/null +++ b/tasks/prd-interactive-ui.md @@ -0,0 +1,238 @@ +# PRD: Interactive Backtest UI + +## 1. Introduction / Overview + +Lowkey Backtest currently operates exclusively through a CLI. This project adds an interactive web-based UI that allows users to: + +- Run backtests with existing code-defined strategies +- Adjust strategy parameters without editing code +- Compare multiple backtest runs visually +- Analyze performance through interactive charts and metrics + +The UI will use **FastAPI** for the backend API and **Vue 3** for the frontend, styled with a dark theme inspired by QuantConnect. + +--- + +## 2. Goals + +| # | Goal | Success Metric | +|---|------|----------------| +| G1 | Run backtests from UI | User can select strategy, symbol, parameters and execute backtest | +| G2 | Visualize results | Equity curves, trade markers, and metrics displayed interactively | +| G3 | Compare runs | Side-by-side comparison of 2+ backtest results | +| G4 | Parameter experimentation | Adjust parameters via form inputs, re-run, and compare to previous | +| G5 | Self-hostable | Deployable on user's Arch-based NAS with minimal configuration | + +--- + +## 3. User Stories + +### US1: Run a Backtest +> As a trader, I want to select a strategy and configure its parameters in the UI so that I can run a backtest without using the terminal. + +### US2: View Backtest Results +> As a trader, I want to see an interactive equity curve with trade entry/exit markers so that I can visually understand strategy performance. + +### US3: Compare Parameter Changes +> As a trader, I want to compare two or more backtest runs side-by-side so that I can determine if a parameter optimization improves performance. + +### US4: Quick Parameter Tweaking +> As a trader, I want to adjust strategy parameters (e.g., period, multiplier, SL/TP) via sliders or inputs and re-run the backtest so that I can iterate quickly. + +### US5: Review Historical Runs +> As a trader, I want to see a list of previous backtest runs so that I can reload and compare past results. + +--- + +## 4. Functional Requirements + +### 4.1 Backend (FastAPI) + +| # | Requirement | +|---|-------------| +| FR1 | `GET /api/strategies` - Return list of available strategies with their default and grid parameters | +| FR2 | `GET /api/symbols` - Return list of available symbols from local data | +| FR3 | `POST /api/backtest` - Execute a backtest with given strategy, symbol, timeframe, and parameters. Return results including portfolio stats, trades, and equity curve data | +| FR4 | `GET /api/backtest/{run_id}` - Retrieve a specific historical backtest result | +| FR5 | `GET /api/backtests` - List all saved backtest runs with summary metadata | +| FR6 | `POST /api/compare` - Accept multiple run IDs and return combined data for comparison | +| FR7 | `GET /api/data/status` - Return available data inventory (symbols, timeframes, date ranges) | +| FR8 | Results must be persisted (SQLite or JSON files) to allow historical retrieval | + +### 4.2 Frontend (Vue 3) + +| # | Requirement | +|---|-------------| +| FR9 | **Strategy Selector**: Dropdown to select strategy (rsi, macross, meta_st, regime) | +| FR10 | **Parameter Form**: Dynamic form fields based on selected strategy's parameters (inputs, sliders) | +| FR11 | **Symbol/Market Picker**: Select trading pair and market type (spot/perpetual) | +| FR12 | **Date Range Selector**: Pick start and end dates for backtest period | +| FR13 | **Run Button**: Execute backtest with loading state indicator | +| FR14 | **Equity Curve Chart**: Interactive line chart (Plotly.js) showing portfolio value over time with drawdown overlay (shaded area) | +| FR15 | **Trade Markers**: Overlay entry (green) and exit (red) markers on price/equity chart | +| FR16 | **Metrics Panel**: Display key stats (Total Return, Sharpe, Max DD, Win Rate, etc.) | +| FR17 | **Trade Log Table**: Sortable, filterable table of all trades | +| FR18 | **Run History Sidebar**: List of previous runs with quick-load action | +| FR19 | **Comparison View**: Select 2+ runs to overlay equity curves and compare metrics in a table | +| FR20 | **Dark Theme**: QuantConnect-inspired dark color scheme | + +### 4.3 Comparison Features + +| # | Requirement | +|---|-------------| +| FR21 | Overlay up to 5 equity curves on the same chart with different colors | +| FR22 | Metrics comparison table showing deltas between runs | +| FR23 | Highlight which run performed better for each metric | +| FR24 | Display parameter differences between compared runs | + +--- + +## 5. Non-Goals (Out of Scope) + +| # | Non-Goal | +|---|----------| +| NG1 | Strategy code editing in the UI (strategies are created in code editor) | +| NG2 | Live trading or paper trading integration | +| NG3 | Real-time market data streaming | +| NG4 | Multi-user authentication or access control | +| NG5 | Walk-Forward Analysis UI (may be added in future iteration) | +| NG6 | Data download management (use CLI for now) | + +--- + +## 6. Design Considerations + +### 6.1 Visual Style +- **Theme**: Dark mode only (similar to QuantConnect, TradingView dark) +- **Colors**: + - Background: `#1a1a2e` (deep navy) + - Cards/Panels: `#16213e` + - Accent: `#0f3460` (muted blue) + - Profit: `#00d26a` (green) + - Loss: `#ff6b6b` (red) + - Text: `#e8e8e8` +- **Typography**: Monospace for numbers/stats, clean sans-serif for labels +- **Layout**: Sidebar navigation + main content area + +### 6.2 Component Library +- Consider Tailwind CSS for styling flexibility +- Plotly.js for charts (matches existing VectorBT ecosystem) +- Headless UI or Radix for accessible components + +### 6.3 Responsive Design +- Primary target: Desktop (1920x1080+) +- Minimum supported: 1280x720 +- Mobile not required + +--- + +## 7. Technical Considerations + +### 7.1 Architecture + +``` +lowkey_backtest/ + api/ # FastAPI backend + __init__.py + main.py # FastAPI app entry point + routers/ + backtest.py # Backtest endpoints + strategies.py # Strategy info endpoints + data.py # Data status endpoints + models/ + schemas.py # Pydantic request/response models + services/ + runner.py # Wraps existing Backtester + storage.py # Run persistence (SQLite) + frontend/ # Vue 3 app (Vite) + src/ + components/ # Reusable UI components + views/ # Page-level components + composables/ # Vue composition functions + api/ # Axios API client + assets/ # Styles, images +``` + +### 7.2 Data Flow + +1. Frontend requests available strategies via `GET /api/strategies` +2. User configures parameters and clicks "Run Backtest" +3. Frontend sends `POST /api/backtest` with configuration +4. Backend instantiates strategy, runs `Backtester.run_strategy()`, saves result +5. Backend returns serialized results (equity curve as JSON array, trades, stats) +6. Frontend renders charts and metrics +7. Run is saved and appears in history sidebar + +### 7.3 Dependencies + +**Backend:** +- FastAPI +- Uvicorn (ASGI server) +- SQLAlchemy (run storage) +- Existing engine modules (Backtester, DataManager, etc.) + +**Frontend:** +- Vue 3 (Composition API) +- Vite (build tool) +- Plotly.js (charts) +- Tailwind CSS (styling) +- Axios (API calls) + +### 7.4 Deployment + +- Development: `uvicorn api.main:app --reload` + `npm run dev` +- Production (NAS): + - Build frontend static files + - Serve via FastAPI's `StaticFiles` or nginx + - Run with systemd service + +--- + +## 8. Success Metrics + +| Metric | Target | +|--------|--------| +| Backtest execution via UI | Works for all 4 strategies | +| Result visualization | Equity curve + trades render correctly | +| Comparison view | Can compare up to 5 runs with overlaid equity curves | +| Run persistence | Historical runs survive app restart | +| Page load time | < 2s for dashboard | +| Backtest response time | Same as CLI (backend performance unchanged) | + +--- + +## 9. Resolved Questions + +| # | Question | Decision | +|---|----------|----------| +| Q1 | React or Vue for frontend? | **Vue 3** - simpler, sufficient for this use case | +| Q2 | Should the equity curve include drawdown overlay by default? | **Yes** - shaded area beneath equity curve | +| Q3 | How many historical runs to keep before auto-cleanup? | **Unlimited** - manual deletion only | +| Q4 | Should comparison support more than 2 runs? | **Yes** - support 3-5 run overlays | +| Q5 | Include WFA visualization in V1 or defer to V2? | Deferred to V2 | + +--- + +## 10. Milestones (Suggested) + +| Phase | Deliverable | Effort | +|-------|-------------|--------| +| **Phase 1** | FastAPI backend with core endpoints (strategies, backtest, history) | 2-3 days | +| **Phase 2** | Frontend scaffold with dark theme and strategy selector | 2-3 days | +| **Phase 3** | Backtest execution flow and results visualization | 3-4 days | +| **Phase 4** | Run history and comparison view | 2-3 days | +| **Phase 5** | Polish, testing, NAS deployment | 2 days | + +**Total Estimated Effort**: 11-15 days + +--- + +## Appendix: Reference Screenshots + +*(QuantConnect-style inspiration)* + +- Dark navy background with card-based layout +- Left sidebar for navigation and run history +- Main area split: configuration panel (top/left) + results (bottom/right) +- Charts with grid lines, legend, and interactive tooltips +- Metrics displayed in a compact card grid diff --git a/uv.lock b/uv.lock index 99888dc..2fc64c3 100644 --- a/uv.lock +++ b/uv.lock @@ -125,6 +125,37 @@ wheels = [ 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