sliding fit fix
This commit is contained in:
Oleg Sheynin
+102
-95
@@ -1,28 +1,30 @@
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from typing import Any, Dict, List
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import pandas as pd
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import sqlite3
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import os
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from datetime import datetime, date
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import sqlite3
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from datetime import date, datetime
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from typing import Any, Dict, List, Optional, Tuple
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import pandas as pd
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from pt_trading.trading_pair import TradingPair
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# Recommended replacement adapters and converters for Python 3.12+
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# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
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def adapt_date_iso(val):
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def adapt_date_iso(val: date) -> str:
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"""Adapt datetime.date to ISO 8601 date."""
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return val.isoformat()
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def adapt_datetime_iso(val):
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def adapt_datetime_iso(val: datetime) -> str:
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"""Adapt datetime.datetime to timezone-naive ISO 8601 date."""
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return val.isoformat()
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def convert_date(val):
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def convert_date(val: bytes) -> date:
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"""Convert ISO 8601 date to datetime.date object."""
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return datetime.fromisoformat(val.decode()).date()
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def convert_datetime(val):
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def convert_datetime(val: bytes) -> datetime:
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"""Convert ISO 8601 datetime to datetime.datetime object."""
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return datetime.fromisoformat(val.decode())
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@@ -172,7 +174,7 @@ def store_results_in_database(
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if db_path.upper() == "NONE":
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return
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def convert_timestamp(timestamp):
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def convert_timestamp(timestamp: Any) -> Optional[datetime]:
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"""Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
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if timestamp is None:
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return None
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@@ -423,14 +425,14 @@ class BacktestResult:
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def add_trade(
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self,
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pair_nm,
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symbol,
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action,
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price,
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disequilibrium=None,
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scaled_disequilibrium=None,
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timestamp=None,
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):
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pair_nm: str,
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symbol: str,
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action: str,
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price: Any,
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disequilibrium: Optional[float] = None,
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scaled_disequilibrium: Optional[float] = None,
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timestamp: Optional[datetime] = None,
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) -> None:
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"""Add a trade to the results tracking."""
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pair_nm = str(pair_nm)
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@@ -442,11 +444,11 @@ class BacktestResult:
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(action, price, disequilibrium, scaled_disequilibrium, timestamp)
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)
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def add_outstanding_position(self, position: Dict[str, Any]):
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def add_outstanding_position(self, position: Dict[str, Any]) -> None:
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"""Add an outstanding position to tracking."""
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self.outstanding_positions.append(position)
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def add_realized_pnl(self, realized_pnl: float):
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def add_realized_pnl(self, realized_pnl: float) -> None:
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"""Add realized PnL to the total."""
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self.total_realized_pnl += realized_pnl
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@@ -462,14 +464,12 @@ class BacktestResult:
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"""Get all trades."""
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return self.trades
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def clear_trades(self):
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def clear_trades(self) -> None:
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"""Clear all trades (used when processing new files)."""
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self.trades.clear()
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def collect_single_day_results(self, result):
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def collect_single_day_results(self, result: pd.DataFrame) -> None:
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"""Collect and process single day trading results."""
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if result is None:
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return
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print("\n -------------- Suggested Trades ")
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print(result)
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@@ -482,16 +482,16 @@ class BacktestResult:
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scaled_disequilibrium = getattr(row, "scaled_disequilibrium", None)
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timestamp = getattr(row, "time", None)
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self.add_trade(
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pair_nm=row.pair,
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action=action,
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symbol=symbol,
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price=price,
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pair_nm=str(row.pair),
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action=str(action),
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symbol=str(symbol),
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price=float(str(price)),
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disequilibrium=disequilibrium,
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scaled_disequilibrium=scaled_disequilibrium,
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timestamp=timestamp,
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)
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def print_single_day_results(self):
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def print_single_day_results(self) -> None:
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"""Print single day results summary."""
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for pair, symbols in self.trades.items():
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print(f"\n--- {pair} ---")
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@@ -501,7 +501,7 @@ class BacktestResult:
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side, price = trade_data[:2]
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print(f"{symbol} {side} at ${price}")
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def print_results_summary(self, all_results):
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def print_results_summary(self, all_results: Dict[str, Dict[str, Any]]) -> None:
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"""Print summary of all processed files."""
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print("\n====== Summary of All Processed Files ======")
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for filename, data in all_results.items():
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@@ -512,7 +512,7 @@ class BacktestResult:
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)
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print(f"{filename}: {trade_count} trades")
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def calculate_returns(self, all_results: Dict):
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def calculate_returns(self, all_results: Dict[str, Dict[str, Any]]) -> None:
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"""Calculate and print returns by day and pair."""
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print("\n====== Returns By Day and Pair ======")
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@@ -527,80 +527,87 @@ class BacktestResult:
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# Calculate individual symbol returns in the pair
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for symbol, trades in symbols.items():
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if len(trades) >= 2: # Need at least entry and exit
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# Get entry and exit trades - handle both old and new tuple formats
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if len(trades[0]) == 2: # Old format: (action, price)
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entry_action, entry_price = trades[0]
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exit_action, exit_price = trades[1]
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open_disequilibrium = None
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open_scaled_disequilibrium = None
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close_disequilibrium = None
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close_scaled_disequilibrium = None
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if len(trades) == 0:
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continue
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symbol_return = 0
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symbol_trades = []
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# Process all trades sequentially for this symbol
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for i, trade in enumerate(trades):
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# Handle both old and new tuple formats
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if len(trade) == 2: # Old format: (action, price)
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action, price = trade
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disequilibrium = None
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scaled_disequilibrium = None
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timestamp = None
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else: # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp)
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entry_action, entry_price = trades[0][:2]
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exit_action, exit_price = trades[1][:2]
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open_disequilibrium = (
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trades[0][2] if len(trades[0]) > 2 else None
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)
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open_scaled_disequilibrium = (
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trades[0][3] if len(trades[0]) > 3 else None
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)
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close_disequilibrium = (
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trades[1][2] if len(trades[1]) > 2 else None
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)
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close_scaled_disequilibrium = (
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trades[1][3] if len(trades[1]) > 3 else None
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)
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# Calculate return based on action
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symbol_return = 0
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if entry_action == "BUY" and exit_action == "SELL":
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action, price = trade[:2]
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disequilibrium = trade[2] if len(trade) > 2 else None
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scaled_disequilibrium = trade[3] if len(trade) > 3 else None
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timestamp = trade[4] if len(trade) > 4 else None
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symbol_trades.append((action, price, disequilibrium, scaled_disequilibrium, timestamp))
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# Calculate returns for all trade combinations
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for i in range(len(symbol_trades) - 1):
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trade1 = symbol_trades[i]
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trade2 = symbol_trades[i + 1]
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action1, price1, diseq1, scaled_diseq1, ts1 = trade1
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action2, price2, diseq2, scaled_diseq2, ts2 = trade2
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# Calculate return based on action combination
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trade_return = 0
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if action1 == "BUY" and action2 == "SELL":
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# Long position
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symbol_return = (
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(exit_price - entry_price) / entry_price * 100
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)
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elif entry_action == "SELL" and exit_action == "BUY":
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trade_return = (price2 - price1) / price1 * 100
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elif action1 == "SELL" and action2 == "BUY":
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# Short position
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symbol_return = (
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(entry_price - exit_price) / entry_price * 100
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)
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trade_return = (price1 - price2) / price1 * 100
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symbol_return += trade_return
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# Store trade details for reporting
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pair_trades.append(
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(
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symbol,
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entry_action,
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entry_price,
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exit_action,
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exit_price,
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symbol_return,
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open_scaled_disequilibrium,
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close_scaled_disequilibrium,
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action1,
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price1,
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action2,
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price2,
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trade_return,
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scaled_diseq1,
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scaled_diseq2,
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i + 1, # Trade sequence number
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)
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)
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pair_return += symbol_return
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pair_return += symbol_return
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# Print pair returns with disequilibrium information
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if pair_trades:
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print(f" {pair}:")
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for (
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symbol,
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entry_action,
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entry_price,
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exit_action,
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exit_price,
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symbol_return,
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open_scaled_disequilibrium,
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close_scaled_disequilibrium,
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action1,
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price1,
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action2,
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price2,
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trade_return,
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scaled_diseq1,
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scaled_diseq2,
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trade_num,
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) in pair_trades:
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disequil_info = ""
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if (
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open_scaled_disequilibrium is not None
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and close_scaled_disequilibrium is not None
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scaled_diseq1 is not None
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and scaled_diseq2 is not None
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):
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disequil_info = f" | Open Dis-eq: {open_scaled_disequilibrium:.2f}, Close Dis-eq: {close_scaled_disequilibrium:.2f}"
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disequil_info = f" | Open Dis-eq: {scaled_diseq1:.2f}, Close Dis-eq: {scaled_diseq2:.2f}"
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print(
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f" {symbol}: {entry_action} @ ${entry_price:.2f}, {exit_action} @ ${exit_price:.2f}, Return: {symbol_return:.2f}%{disequil_info}"
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f" {symbol} (Trade #{trade_num}): {action1} @ ${price1:.2f}, {action2} @ ${price2:.2f}, Return: {trade_return:.2f}%{disequil_info}"
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)
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print(f" Pair Total Return: {pair_return:.2f}%")
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day_return += pair_return
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@@ -610,7 +617,7 @@ class BacktestResult:
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print(f" Day Total Return: {day_return:.2f}%")
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self.add_realized_pnl(day_return)
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def print_outstanding_positions(self):
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def print_outstanding_positions(self) -> None:
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"""Print all outstanding positions with share quantities and current values."""
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if not self.get_outstanding_positions():
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print("\n====== NO OUTSTANDING POSITIONS ======")
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@@ -684,22 +691,22 @@ class BacktestResult:
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print(f"{'TOTAL OUTSTANDING VALUE':<80} ${total_value:<12.2f}")
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def print_grand_totals(self):
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def print_grand_totals(self) -> None:
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"""Print grand totals across all pairs."""
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print(f"\n====== GRAND TOTALS ACROSS ALL PAIRS ======")
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print(f"Total Realized PnL: {self.get_total_realized_pnl():.2f}%")
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def handle_outstanding_position(
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self,
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pair,
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pair_result_df,
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last_row_index,
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open_side_a,
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open_side_b,
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open_px_a,
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open_px_b,
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open_tstamp,
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):
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pair: TradingPair,
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pair_result_df: pd.DataFrame,
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last_row_index: int,
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open_side_a: str,
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open_side_b: str,
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open_px_a: float,
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open_px_b: float,
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open_tstamp: datetime,
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) -> Tuple[float, float, float]:
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"""
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Handle calculation and tracking of outstanding positions when no close signal is found.
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