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sliding fit fix

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This commit is contained in:
Oleg Sheynin 2025-07-13 22:33:48 +00:00
parent 48f18f7b4f
commit b24285802a
6 changed files with 553 additions and 297 deletions
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26
configuration/equity_single.cfg Normal file
View File

@ -0,0 +1,26 @@
{
"security_type": "EQUITY",
"data_directory": "./data/equity",
"datafiles": [
"20250605.mktdata.ohlcv.db",
],
"db_table_name": "md_1min_bars",
"exchange_id": "ALPACA",
"instrument_id_pfx": "STOCK-",
"trading_hours": {
"begin_session": "9:30:00",
"end_session": "16:00:00",
"timezone": "America/New_York"
},
"price_column": "close",
"min_required_points": 30,
"zero_threshold": 1e-10,
"dis-equilibrium_open_trshld": 2.0,
"dis-equilibrium_close_trshld": 1.0,
"training_minutes": 120,
"funding_per_pair": 2000.0,
"fit_method_class": "pt_trading.fit_methods.SlidingFit",
# "fit_method_class": "pt_trading.fit_methods.StaticFit",
"exclude_instruments": ["CAN"]
}

134
lib/pt_trading/fit_methods.py
View File

@ -9,6 +9,7 @@ from pt_trading.trading_pair import TradingPair
NanoPerMin = 1e9
class PairsTradingFitMethod(ABC):
TRADES_COLUMNS = [
"time",
@ -19,17 +20,21 @@ class PairsTradingFitMethod(ABC):
"scaled_disequilibrium",
"pair",
]
@abstractmethod
def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]:
...
@abstractmethod
def reset(self):
...
def run_pair(
self, config: Dict, pair: TradingPair, bt_result: BacktestResult
) -> Optional[pd.DataFrame]: ...
@abstractmethod
def reset(self) -> None: ...
class StaticFit(PairsTradingFitMethod):
def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]: # abstractmethod
def run_pair(
self, config: Dict, pair: TradingPair, bt_result: BacktestResult
) -> Optional[pd.DataFrame]: # abstractmethod
pair.get_datasets(training_minutes=config["training_minutes"])
try:
is_cointegrated = pair.train_pair()
@ -46,11 +51,15 @@ class StaticFit(PairsTradingFitMethod):
print(f"{pair}: Prediction failed: {str(e)}")
return None
pair_trades = self.create_trading_signals(pair=pair, config=config, result=bt_result)
pair_trades = self.create_trading_signals(
pair=pair, config=config, result=bt_result
)
return pair_trades
def create_trading_signals(self, pair: TradingPair, config: Dict, result: BacktestResult) -> pd.DataFrame:
def create_trading_signals(
self, pair: TradingPair, config: Dict, result: BacktestResult
) -> pd.DataFrame:
beta = pair.vecm_fit_.beta # type: ignore
colname_a, colname_b = pair.colnames()
@ -205,39 +214,45 @@ class StaticFit(PairsTradingFitMethod):
def reset(self) -> None:
pass
class PairState(Enum):
INITIAL = 1
OPEN = 2
CLOSED = 3
class SlidingFit(PairsTradingFitMethod):
def __init__(self) -> None:
super().__init__()
self.curr_training_start_idx_ = 0
def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]:
def run_pair(
self, config: Dict, pair: TradingPair, bt_result: BacktestResult
) -> Optional[pd.DataFrame]:
print(f"***{pair}*** STARTING....")
pair.user_data_['state'] = PairState.INITIAL
pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS) # type: ignore
pair.user_data_["state"] = PairState.INITIAL
pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS)
pair.user_data_["is_cointegrated"] = False
open_threshold = config["dis-equilibrium_open_trshld"]
close_threshold = config["dis-equilibrium_open_trshld"]
training_minutes = config["training_minutes"]
curr_predicted_row_idx = 0
while True:
print(self.curr_training_start_idx_, end='\r')
print(self.curr_training_start_idx_, end="\r")
pair.get_datasets(
training_minutes=training_minutes,
training_start_index=self.curr_training_start_idx_,
testing_size=1
testing_size=1,
)
if len(pair.training_df_) < training_minutes:
print(f"{pair}: {self.curr_training_start_idx_} Not enough training data. Completing the job.")
print(
f"{pair}: {self.curr_training_start_idx_} Not enough training data. Completing the job."
)
if pair.user_data_["state"] == PairState.OPEN:
print(f"{pair}: {self.curr_training_start_idx_} Position is not closed.")
print(
f"{pair}: {self.curr_training_start_idx_} Position is not closed."
)
# outstanding positions
# last_row_index = self.curr_training_start_idx_ + training_minutes
@ -262,13 +277,19 @@ class SlidingFit(PairsTradingFitMethod):
pair.user_data_["is_cointegrated"] = is_cointegrated
if not is_cointegrated:
if pair.user_data_["state"] == PairState.OPEN:
print(f"{pair} {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions...")
print(
f"{pair} {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions..."
)
else:
print(f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on")
print(
f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on"
)
else:
print('*' * 80)
print(f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED")
print('*' * 80)
print("*" * 80)
print(
f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED"
)
print("*" * 80)
if not is_cointegrated:
self.curr_training_start_idx_ += 1
continue
@ -278,34 +299,55 @@ class SlidingFit(PairsTradingFitMethod):
except Exception as e:
raise RuntimeError(f"{pair}: Prediction failed: {str(e)}") from e
if pair.user_data_["state"] == PairState.INITIAL:
open_trades = self._get_open_trades(pair, open_threshold=open_threshold)
if open_trades is not None:
pair.user_data_["trades"] = open_trades
pair.user_data_["state"] = PairState.OPEN
elif pair.user_data_["state"] == PairState.OPEN:
close_trades = self._get_close_trades(pair, close_threshold=close_threshold)
if close_trades is not None:
pair.user_data_["trades"] = pd.concat([pair.user_data_["trades"], close_trades], ignore_index=True)
pair.user_data_["state"] = PairState.CLOSED
break
# break
self.curr_training_start_idx_ += 1
curr_predicted_row_idx += 1
self._create_trading_signals(pair, config, bt_result)
print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
return pair.user_data_["trades"]
return pair.get_trades()
def _get_open_trades(self, pair: TradingPair, open_threshold: float) -> Optional[pd.DataFrame]:
def _create_trading_signals(
self, pair: TradingPair, config: Dict, bt_result: BacktestResult
) -> None:
assert pair.predicted_df_ is not None
open_threshold = config["dis-equilibrium_open_trshld"]
close_threshold = config["dis-equilibrium_close_trshld"]
for curr_predicted_row_idx in range(len(pair.predicted_df_)):
pred_row = pair.predicted_df_.iloc[curr_predicted_row_idx]
if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSED]:
open_trades = self._get_open_trades(
pair, row=pred_row, open_threshold=open_threshold
)
if open_trades is not None:
open_trades["status"] = "OPEN"
print(f"OPEN TRADES:\n{open_trades}")
pair.add_trades(open_trades)
pair.user_data_["state"] = PairState.OPEN
elif pair.user_data_["state"] == PairState.OPEN:
close_trades = self._get_close_trades(
pair, row=pred_row, close_threshold=close_threshold
)
if close_trades is not None:
close_trades["status"] = "CLOSE"
print(f"CLOSE TRADES:\n{close_trades}")
pair.add_trades(close_trades)
pair.user_data_["state"] = PairState.CLOSED
def _get_open_trades(
self, pair: TradingPair, row: pd.Series, open_threshold: float
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.colnames()
assert pair.predicted_df_ is not None
predicted_df = pair.predicted_df_
# Check if we have any data to work with
if len(predicted_df) == 0:
return None
open_row = predicted_df.iloc[0]
open_row = row
open_tstamp = open_row["tstamp"]
open_disequilibrium = open_row["disequilibrium"]
open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
@ -316,6 +358,7 @@ class SlidingFit(PairsTradingFitMethod):
return None
# creating the trades
print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}")
if open_disequilibrium > 0:
open_side_a = "SELL"
open_side_b = "BUY"
@ -338,7 +381,6 @@ class SlidingFit(PairsTradingFitMethod):
pair.user_data_["close_side_a"] = close_side_a
pair.user_data_["close_side_b"] = close_side_b
# create opening trades
trd_signal_tuples = [
(
@ -365,14 +407,16 @@ class SlidingFit(PairsTradingFitMethod):
columns=self.TRADES_COLUMNS, # type: ignore
)
def _get_close_trades(self, pair: TradingPair, close_threshold: float) -> Optional[pd.DataFrame]:
def _get_close_trades(
self, pair: TradingPair, row: pd.Series, close_threshold: float
) -> Optional[pd.DataFrame]:
colname_a, colname_b = pair.colnames()
# Check if we have any data to work with
assert pair.predicted_df_ is not None
if len(pair.predicted_df_) == 0:
return None
close_row = pair.predicted_df_.iloc[0]
close_row = row
close_tstamp = close_row["tstamp"]
close_disequilibrium = close_row["disequilibrium"]
close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
@ -384,7 +428,6 @@ class SlidingFit(PairsTradingFitMethod):
if close_scaled_disequilibrium > close_threshold:
return None
trd_signal_tuples = [
(
close_tstamp,
@ -412,8 +455,5 @@ class SlidingFit(PairsTradingFitMethod):
columns=self.TRADES_COLUMNS, # type: ignore
)
def reset(self):
def reset(self) -> None:
self.curr_training_start_idx_ = 0

195
lib/pt_trading/results.py
View File

@ -1,28 +1,30 @@
from typing import Any, Dict, List
import pandas as pd
import sqlite3
import os
from datetime import datetime, date
import sqlite3
from datetime import date, datetime
from typing import Any, Dict, List, Optional, Tuple
import pandas as pd
from pt_trading.trading_pair import TradingPair
# Recommended replacement adapters and converters for Python 3.12+
# From: https://docs.python.org/3/library/sqlite3.html#sqlite3-adapter-converter-recipes
def adapt_date_iso(val):
def adapt_date_iso(val: date) -> str:
"""Adapt datetime.date to ISO 8601 date."""
return val.isoformat()
def adapt_datetime_iso(val):
def adapt_datetime_iso(val: datetime) -> str:
"""Adapt datetime.datetime to timezone-naive ISO 8601 date."""
return val.isoformat()
def convert_date(val):
def convert_date(val: bytes) -> date:
"""Convert ISO 8601 date to datetime.date object."""
return datetime.fromisoformat(val.decode()).date()
def convert_datetime(val):
def convert_datetime(val: bytes) -> datetime:
"""Convert ISO 8601 datetime to datetime.datetime object."""
return datetime.fromisoformat(val.decode())
@ -172,7 +174,7 @@ def store_results_in_database(
if db_path.upper() == "NONE":
return
def convert_timestamp(timestamp):
def convert_timestamp(timestamp: Any) -> Optional[datetime]:
"""Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
if timestamp is None:
return None
@ -423,14 +425,14 @@ class BacktestResult:
def add_trade(
self,
pair_nm,
symbol,
action,
price,
disequilibrium=None,
scaled_disequilibrium=None,
timestamp=None,
):
pair_nm: str,
symbol: str,
action: str,
price: Any,
disequilibrium: Optional[float] = None,
scaled_disequilibrium: Optional[float] = None,
timestamp: Optional[datetime] = None,
) -> None:
"""Add a trade to the results tracking."""
pair_nm = str(pair_nm)
@ -442,11 +444,11 @@ class BacktestResult:
(action, price, disequilibrium, scaled_disequilibrium, timestamp)
)
def add_outstanding_position(self, position: Dict[str, Any]):
def add_outstanding_position(self, position: Dict[str, Any]) -> None:
"""Add an outstanding position to tracking."""
self.outstanding_positions.append(position)
def add_realized_pnl(self, realized_pnl: float):
def add_realized_pnl(self, realized_pnl: float) -> None:
"""Add realized PnL to the total."""
self.total_realized_pnl += realized_pnl
@ -462,14 +464,12 @@ class BacktestResult:
"""Get all trades."""
return self.trades
def clear_trades(self):
def clear_trades(self) -> None:
"""Clear all trades (used when processing new files)."""
self.trades.clear()
def collect_single_day_results(self, result):
def collect_single_day_results(self, result: pd.DataFrame) -> None:
"""Collect and process single day trading results."""
if result is None:
return
print("\n -------------- Suggested Trades ")
print(result)
@ -482,16 +482,16 @@ class BacktestResult:
scaled_disequilibrium = getattr(row, "scaled_disequilibrium", None)
timestamp = getattr(row, "time", None)
self.add_trade(
pair_nm=row.pair,
action=action,
symbol=symbol,
price=price,
pair_nm=str(row.pair),
action=str(action),
symbol=str(symbol),
price=float(str(price)),
disequilibrium=disequilibrium,
scaled_disequilibrium=scaled_disequilibrium,
timestamp=timestamp,
)
def print_single_day_results(self):
def print_single_day_results(self) -> None:
"""Print single day results summary."""
for pair, symbols in self.trades.items():
print(f"\n--- {pair} ---")
@ -501,7 +501,7 @@ class BacktestResult:
side, price = trade_data[:2]
print(f"{symbol} {side} at ${price}")
def print_results_summary(self, all_results):
def print_results_summary(self, all_results: Dict[str, Dict[str, Any]]) -> None:
"""Print summary of all processed files."""
print("\n====== Summary of All Processed Files ======")
for filename, data in all_results.items():
@ -512,7 +512,7 @@ class BacktestResult:
)
print(f"{filename}: {trade_count} trades")
def calculate_returns(self, all_results: Dict):
def calculate_returns(self, all_results: Dict[str, Dict[str, Any]]) -> None:
"""Calculate and print returns by day and pair."""
print("\n====== Returns By Day and Pair ======")
@ -527,56 +527,62 @@ class BacktestResult:
# Calculate individual symbol returns in the pair
for symbol, trades in symbols.items():
if len(trades) >= 2: # Need at least entry and exit
# Get entry and exit trades - handle both old and new tuple formats
if len(trades[0]) == 2: # Old format: (action, price)
entry_action, entry_price = trades[0]
exit_action, exit_price = trades[1]
open_disequilibrium = None
open_scaled_disequilibrium = None
close_disequilibrium = None
close_scaled_disequilibrium = None
else: # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp)
entry_action, entry_price = trades[0][:2]
exit_action, exit_price = trades[1][:2]
open_disequilibrium = (
trades[0][2] if len(trades[0]) > 2 else None
)
open_scaled_disequilibrium = (
trades[0][3] if len(trades[0]) > 3 else None
)
close_disequilibrium = (
trades[1][2] if len(trades[1]) > 2 else None
)
close_scaled_disequilibrium = (
trades[1][3] if len(trades[1]) > 3 else None
)
if len(trades) == 0:
continue
# Calculate return based on action
symbol_return = 0
if entry_action == "BUY" and exit_action == "SELL":
# Long position
symbol_return = (
(exit_price - entry_price) / entry_price * 100
)
elif entry_action == "SELL" and exit_action == "BUY":
# Short position
symbol_return = (
(entry_price - exit_price) / entry_price * 100
)
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))
# 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
# Calculate return based on action combination
trade_return = 0
if action1 == "BUY" and action2 == "SELL":
# Long position
trade_return = (price2 - price1) / price1 * 100
elif action1 == "SELL" and action2 == "BUY":
# Short position
trade_return = (price1 - price2) / price1 * 100
symbol_return += trade_return
# Store trade details for reporting
pair_trades.append(
(
symbol,
entry_action,
entry_price,
exit_action,
exit_price,
symbol_return,
open_scaled_disequilibrium,
close_scaled_disequilibrium,
action1,
price1,
action2,
price2,
trade_return,
scaled_diseq1,
scaled_diseq2,
i + 1, # Trade sequence number
)
)
pair_return += symbol_return
# Print pair returns with disequilibrium information
@ -584,23 +590,24 @@ class BacktestResult:
print(f" {pair}:")
for (
symbol,
entry_action,
entry_price,
exit_action,
exit_price,
symbol_return,
open_scaled_disequilibrium,
close_scaled_disequilibrium,
action1,
price1,
action2,
price2,
trade_return,
scaled_diseq1,
scaled_diseq2,
trade_num,
) in pair_trades:
disequil_info = ""
if (
open_scaled_disequilibrium is not None
and close_scaled_disequilibrium is not None
scaled_diseq1 is not None
and scaled_diseq2 is not None
):
disequil_info = f" | Open Dis-eq: {open_scaled_disequilibrium:.2f}, Close Dis-eq: {close_scaled_disequilibrium:.2f}"
disequil_info = f" | Open Dis-eq: {scaled_diseq1:.2f}, Close Dis-eq: {scaled_diseq2:.2f}"
print(
f" {symbol}: {entry_action} @ ${entry_price:.2f}, {exit_action} @ ${exit_price:.2f}, Return: {symbol_return:.2f}%{disequil_info}"
f" {symbol} (Trade #{trade_num}): {action1} @ ${price1:.2f}, {action2} @ ${price2:.2f}, Return: {trade_return:.2f}%{disequil_info}"
)
print(f" Pair Total Return: {pair_return:.2f}%")
day_return += pair_return
@ -610,7 +617,7 @@ class BacktestResult:
print(f" Day Total Return: {day_return:.2f}%")
self.add_realized_pnl(day_return)
def print_outstanding_positions(self):
def print_outstanding_positions(self) -> None:
"""Print all outstanding positions with share quantities and current values."""
if not self.get_outstanding_positions():
print("\n====== NO OUTSTANDING POSITIONS ======")
@ -684,22 +691,22 @@ class BacktestResult:
print(f"{'TOTAL OUTSTANDING VALUE':<80} ${total_value:<12.2f}")
def print_grand_totals(self):
def print_grand_totals(self) -> None:
"""Print grand totals across all pairs."""
print(f"\n====== GRAND TOTALS ACROSS ALL PAIRS ======")
print(f"Total Realized PnL: {self.get_total_realized_pnl():.2f}%")
def handle_outstanding_position(
self,
pair,
pair_result_df,
last_row_index,
open_side_a,
open_side_b,
open_px_a,
open_px_b,
open_tstamp,
):
pair: TradingPair,
pair_result_df: pd.DataFrame,
last_row_index: int,
open_side_a: str,
open_side_b: str,
open_px_a: float,
open_px_b: float,
open_tstamp: datetime,
) -> Tuple[float, float, float]:
"""
Handle calculation and tracking of outstanding positions when no close signal is found.

70
lib/pt_trading/trading_pair.py
View File

@ -1,4 +1,5 @@
from typing import Any, Dict, List, Optional
import pandas as pd # type:ignore
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults # type:ignore
@ -19,6 +20,8 @@ class TradingPair:
user_data_: Dict[str, Any]
predicted_df_: Optional[pd.DataFrame]
def __init__(
self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
):
@ -31,7 +34,7 @@ class TradingPair:
self.user_data_ = {}
self.predicted_df_ = pd.DataFrame()
self.predicted_df_ = None
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
# Select only the columns we need
@ -127,9 +130,9 @@ class TradingPair:
df = self.training_df_[self.colnames()].reset_index(drop=True)
result = coint_johansen(df, det_order=0, k_ar_diff=1)
print(
f"{self}: lr1={result.lr1[0]} > cvt={result.cvt[0, 1]}? {result.lr1[0] > result.cvt[0, 1]}"
)
# print(
# f"{self}: lr1={result.lr1[0]} > cvt={result.cvt[0, 1]}? {result.lr1[0] > result.cvt[0, 1]}"
# )
is_cointegrated: bool = bool(result.lr1[0] > result.cvt[0, 1])
return is_cointegrated
@ -146,21 +149,22 @@ class TradingPair:
pvalue = coint(series1, series2)[1]
# Define cointegration if p-value < 0.05 (i.e., reject null of no cointegration)
is_cointegrated: bool = bool(pvalue < 0.05)
print(f"{self}: is_cointegrated={is_cointegrated} pvalue={pvalue}")
# print(f"{self}: is_cointegrated={is_cointegrated} pvalue={pvalue}")
return is_cointegrated
def train_pair(self) -> bool:
def check_cointegration(self) -> bool:
is_cointegrated_johansen = self.check_cointegration_johansen()
is_cointegrated_engle_granger = self.check_cointegration_engle_granger()
if not is_cointegrated_johansen and not is_cointegrated_engle_granger:
return False
pass
result = is_cointegrated_johansen or is_cointegrated_engle_granger
return result or True # TODO: remove this
def train_pair(self) -> bool:
result = self.check_cointegration()
# print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
self.fit_VECM()
assert self.training_df_ is not None and self.vecm_fit_ is not None
diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
print(diseq_series.shape)
# print(diseq_series.shape)
self.training_mu_ = float(diseq_series[0].mean())
self.training_std_ = float(diseq_series[0].std())
@ -172,7 +176,16 @@ class TradingPair:
diseq_series - self.training_mu_
) / self.training_std_
return True
return result
def add_trades(self, trades: pd.DataFrame) -> None:
if self.user_data_["trades"] is None:
self.user_data_["trades"] = pd.DataFrame(trades)
else:
self.user_data_["trades"] = pd.concat([self.user_data_["trades"], pd.DataFrame(trades)], ignore_index=True)
def get_trades(self) -> pd.DataFrame:
return self.user_data_["trades"] if "trades" in self.user_data_ else pd.DataFrame()
def predict(self) -> pd.DataFrame:
assert self.testing_df_ is not None
@ -184,24 +197,6 @@ class TradingPair:
predicted_prices, columns=pd.Index(self.colnames()), dtype=float
)
# self.predicted_df_ = pd.merge(
# self.testing_df_.reset_index(drop=True),
# pd.DataFrame(
# predicted_prices, columns=pd.Index(self.colnames()), dtype=float
# ),
# left_index=True,
# right_index=True,
# suffixes=("", "_pred"),
# ).dropna()
# self.predicted_df_["disequilibrium"] = (
# self.predicted_df_[self.colnames()] @ self.vecm_fit_.beta
# )
# self.predicted_df_["scaled_disequilibrium"] = (
# abs(self.predicted_df_["disequilibrium"] - self.training_mu_)
# / self.training_std_
# )
predicted_df = pd.merge(
self.testing_df_.reset_index(drop=True),
@ -222,17 +217,20 @@ class TradingPair:
/ self.training_std_
)
print("*** PREDICTED DF")
print(predicted_df)
print("*" * 80)
print("*** SELF.PREDICTED_DF")
print(self.predicted_df_)
print("*" * 80)
# print("*** PREDICTED DF")
# print(predicted_df)
# print("*" * 80)
# print("*** SELF.PREDICTED_DF")
# print(self.predicted_df_)
# print("*" * 80)
predicted_df = predicted_df.reset_index(drop=True)
if self.predicted_df_ is None:
self.predicted_df_ = predicted_df
else:
self.predicted_df_ = pd.concat([self.predicted_df_, predicted_df], ignore_index=True)
# Reset index to ensure proper indexing
self.predicted_df_ = self.predicted_df_.reset_index()
self.predicted_df_ = self.predicted_df_.reset_index(drop=True)
return self.predicted_df_
def __repr__(self) -> str:

407
research/notebooks/pt_pair_backtest.ipynb
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2
research/pt_backtest.py
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@ -99,7 +99,7 @@ def run_backtest(
)
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}")
# Check if result_list has any data before concatenating
if len(pairs_trades) == 0:
print("No trading signals found for any pairs")