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bug fix

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This commit is contained in:
Oleg Sheynin 2025-07-22 17:25:16 +00:00
parent 0e83142d0a
commit 31eb9f800c
7 changed files with 75 additions and 77 deletions
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4
configuration/crypto_zscore.cfg
View File

@ -2,7 +2,7 @@
"security_type": "CRYPTO",
"data_directory": "./data/crypto",
"datafiles": [
"2025*.mktdata.ohlcv.db"
"20250602.mktdata.ohlcv.db"
],
"db_table_name": "md_1min_bars",
"exchange_id": "BNBSPOT",
@ -27,7 +27,7 @@
# "close_outstanding_positions": false,
"trading_hours": {
"begin_session": "9:30:00",
"end_session": "21:30:00",
"end_session": "19:00:00",
"timezone": "America/New_York"
}
}

3
lib/pt_trading/fit_method.py
View File

@ -12,8 +12,9 @@ NanoPerMin = 1e9
class PairsTradingFitMethod(ABC):
TRADES_COLUMNS = [
"time",
"action",
"symbol",
"side",
"action",
"price",
"disequilibrium",
"scaled_disequilibrium",

89
lib/pt_trading/results.py
View File

@ -442,11 +442,13 @@ class BacktestResult:
self,
pair_nm: str,
symbol: str,
side: str,
action: str,
price: Any,
disequilibrium: Optional[float] = None,
scaled_disequilibrium: Optional[float] = None,
timestamp: Optional[datetime] = None,
status: Optional[str] = None,
) -> None:
"""Add a trade to the results tracking."""
pair_nm = str(pair_nm)
@ -456,7 +458,15 @@ class BacktestResult:
if symbol not in self.trades[pair_nm]:
self.trades[pair_nm][symbol] = []
self.trades[pair_nm][symbol].append(
(action, price, disequilibrium, scaled_disequilibrium, timestamp)
{"symbol":symbol,
"side":side,
"action":action,
"price":price,
"disequilibrium":disequilibrium,
"scaled_disequilibrium":scaled_disequilibrium,
"timestamp":timestamp,
"status":status
}
)
def add_outstanding_position(self, position: Dict[str, Any]) -> None:
@ -493,6 +503,7 @@ class BacktestResult:
print(result)
for row in result.itertuples():
side = row.side
action = row.action
symbol = row.symbol
price = row.price
@ -502,15 +513,17 @@ class BacktestResult:
timestamp = getattr(row, "time")
else:
timestamp = convert_timestamp(row.Index)
status = row.status
self.add_trade(
pair_nm=str(row.pair),
action=str(action),
symbol=str(symbol),
side=str(side),
action=str(action),
price=float(str(price)),
disequilibrium=disequilibrium,
scaled_disequilibrium=scaled_disequilibrium,
timestamp=timestamp,
status=str(status) if status is not None else "?",
)
def print_single_day_results(self) -> None:
@ -553,42 +566,22 @@ class BacktestResult:
for symbol, trades in symbols.items():
if len(trades) == 0:
continue
symbol_return = 0
symbol_trades = []
# Process all trades sequentially for this symbol
for i, trade in enumerate(trades):
# Handle both old and new tuple formats
if len(trade) == 2: # Old format: (action, price)
action, price = trade
disequilibrium = None
scaled_disequilibrium = None
timestamp = None
else: # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp)
action, price = trade[:2]
disequilibrium = trade[2] if len(trade) > 2 else None
scaled_disequilibrium = trade[3] if len(trade) > 3 else None
timestamp = trade[4] if len(trade) > 4 else None
symbol_trades.append((action, price, disequilibrium, scaled_disequilibrium, timestamp))
symbol_trades = [trade for trade in trades if trade["symbol"] == symbol]
# Calculate returns for all trade combinations
for i in range(len(symbol_trades) - 1):
trade1 = symbol_trades[i]
trade2 = symbol_trades[i + 1]
action1, price1, diseq1, scaled_diseq1, ts1 = trade1
action2, price2, diseq2, scaled_diseq2, ts2 = trade2
trade1 = trades[i]
trade2 = trades[i + 1]
# Calculate return based on action combination
trade_return = 0
if action1 == "BUY" and action2 == "SELL":
if trade1["side"] == "BUY" and trade2["side"] == "SELL":
# Long position
trade_return = (price2 - price1) / price1 * 100
elif action1 == "SELL" and action2 == "BUY":
trade_return = (trade2["price"] - trade1["price"]) / trade1["price"] * 100
elif trade1["side"] == "SELL" and trade2["side"] == "BUY":
# Short position
trade_return = (price1 - price2) / price1 * 100
trade_return = (trade1["price"] - trade2["price"]) / trade1["price"] * 100
symbol_return += trade_return
@ -596,13 +589,13 @@ class BacktestResult:
pair_trades.append(
(
symbol,
action1,
price1,
action2,
price2,
trade1["side"],
trade1["price"],
trade2["side"],
trade2["price"],
trade_return,
scaled_diseq1,
scaled_diseq2,
trade1["scaled_disequilibrium"],
trade2["scaled_disequilibrium"],
i + 1, # Trade sequence number
)
)
@ -614,24 +607,28 @@ class BacktestResult:
print(f" {pair}:")
for (
symbol,
action1,
price1,
action2,
price2,
trade1["side"],
trade1["price"],
trade2["side"],
trade2["price"],
trade_return,
scaled_diseq1,
scaled_diseq2,
trade1["scaled_disequilibrium"],
trade2["scaled_disequilibrium"],
trade_num,
) in pair_trades:
disequil_info = ""
if (
scaled_diseq1 is not None
and scaled_diseq2 is not None
trade1["scaled_disequilibrium"] is not None
and trade2["scaled_disequilibrium"] is not None
):
disequil_info = f" | Open Dis-eq: {scaled_diseq1:.2f}, Close Dis-eq: {scaled_diseq2:.2f}"
disequil_info = f" | Open Dis-eq: {trade1["scaled_disequilibrium"]:.2f},"
f" Close Dis-eq: {trade2["scaled_disequilibrium"]:.2f}"
print(
f" {symbol} (Trade #{trade_num}): {action1} @ ${price1:.2f}, {action2} @ ${price2:.2f}, Return: {trade_return:.2f}%{disequil_info}"
f" {symbol} (Trade #{trade_num}):"
f" {trade1["side"]} @ ${trade1["price"]:.2f},"
f" {trade2["side"]} @ ${trade2["price"]:.2f},"
f" Return: {trade_return:.2f}%{disequil_info}"
)
print(f" Pair Total Return: {pair_return:.2f}%")
day_return += pair_return

17
lib/pt_trading/rolling_window_fit.py
View File

@ -35,8 +35,9 @@ class RollingFit(PairsTradingFitMethod):
# Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype({
"time": "datetime64[ns]",
"action": "string",
"symbol": "string",
"side": "string",
"action": "string",
"price": "float64",
"disequilibrium": "float64",
"scaled_disequilibrium": "float64",
@ -136,7 +137,7 @@ class RollingFit(PairsTradingFitMethod):
close_position_trades = self._get_close_trades(
pair=pair,
row=pred_row,
close_threshold=close_threshold,
close_threshold=close_threshold
)
if close_position_trades is not None:
close_position_trades["status"] = PairState.CLOSE_POSITION.name
@ -197,8 +198,9 @@ class RollingFit(PairsTradingFitMethod):
trd_signal_tuples = [
(
open_tstamp,
open_side_a,
pair.symbol_a_,
open_side_a,
"OPEN",
open_px_a,
open_disequilibrium,
open_scaled_disequilibrium,
@ -206,8 +208,9 @@ class RollingFit(PairsTradingFitMethod):
),
(
open_tstamp,
open_side_b,
pair.symbol_b_,
open_side_b,
"OPEN",
open_px_b,
open_disequilibrium,
open_scaled_disequilibrium,
@ -248,8 +251,9 @@ class RollingFit(PairsTradingFitMethod):
trd_signal_tuples = [
(
close_tstamp,
close_side_a,
pair.symbol_a_,
close_side_a,
"CLOSE",
close_px_a,
close_disequilibrium,
close_scaled_disequilibrium,
@ -257,8 +261,9 @@ class RollingFit(PairsTradingFitMethod):
),
(
close_tstamp,
close_side_b,
pair.symbol_b_,
close_side_b,
"CLOSE",
close_px_b,
close_disequilibrium,
close_scaled_disequilibrium,

4
lib/pt_trading/vecm_rolling_fit.py
View File

@ -94,10 +94,6 @@ class VECMRollingFit(RollingFit):
def __init__(self) -> None:
super().__init__()
def run_pair(
self, pair: TradingPair, bt_result: BacktestResult
) -> Optional[pd.DataFrame]:
return super().run_pair(pair, bt_result)
def create_trading_pair(
self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
) -> TradingPair:

19
lib/pt_trading/z-score_rolling_fit.py
View File

@ -20,19 +20,18 @@ class ZScoreTradingPair(TradingPair):
def _fit_zscore(self) -> None:
assert self.training_df_ is not None
a = self.training_df_[self.colnames()].iloc[:, 0]
b = self.training_df_[self.colnames()].iloc[:, 1]
symbol_a_px_series = self.training_df_[self.colnames()].iloc[:, 0]
symbol_b_px_series = self.training_df_[self.colnames()].iloc[:, 1]
a,b = a.align(b, axis=0)
symbol_a_px_series,symbol_b_px_series = symbol_a_px_series.align(symbol_b_px_series, axis=0)
X = sm.add_constant(b)
self.zscore_model_ = sm.OLS(a, X).fit()
X = sm.add_constant(symbol_b_px_series)
self.zscore_model_ = sm.OLS(symbol_a_px_series, X).fit()
assert self.zscore_model_ is not None
hedge_ratio = self.zscore_model_.params.iloc[1]
# Calculate spread and Z-score
spread = a - hedge_ratio * b
spread = symbol_a_px_series - hedge_ratio * symbol_b_px_series
self.zscore_df_ = (spread - spread.mean()) / spread.std()
def predict(self) -> pd.DataFrame:
@ -55,17 +54,13 @@ class ZScoreTradingPair(TradingPair):
self.pair_predict_result_ = pd.concat([self.pair_predict_result_, predicted_df], ignore_index=True)
# Reset index to ensure proper indexing
self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
return self.pair_predict_result_
return self.pair_predict_result_.dropna()
class ZScoreRollingFit(RollingFit):
def __init__(self) -> None:
super().__init__()
def run_pair(
self, pair: TradingPair, bt_result: BacktestResult
) -> Optional[pd.DataFrame]:
return super().run_pair(pair, bt_result)
def create_trading_pair(
self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
) -> TradingPair:

14
research/pt_backtest.py
View File

@ -72,13 +72,17 @@ def run_backtest(
bt_result: BacktestResult = BacktestResult(config=config)
pairs_trades = []
for pair in create_pairs(datafile=datafile, fit_method=fit_method, price_column=price_column, config=config, instruments=instruments):
single_pair_trades = fit_method.run_pair(
pair=pair, bt_result=bt_result
)
for pair in create_pairs(
datafile=datafile,
fit_method=fit_method,
price_column=price_column,
config=config,
instruments=instruments,
):
single_pair_trades = fit_method.run_pair(pair=pair, bt_result=bt_result)
if single_pair_trades is not None and len(single_pair_trades) > 0:
pairs_trades.append(single_pair_trades)
print(f"pairs_trades: {pairs_trades}")
print(f"pairs_trades:\n{pairs_trades}")
# Check if result_list has any data before concatenating
if len(pairs_trades) == 0:
print("No trading signals found for any pairs")