progress: stop signals
This commit is contained in:
Oleg Sheynin
+144
-78
@@ -3,9 +3,9 @@ from enum import Enum
|
||||
from typing import Dict, Optional, cast
|
||||
|
||||
import pandas as pd # type: ignore[import]
|
||||
from pt_trading.fit_method import PairState, PairsTradingFitMethod
|
||||
from pt_trading.fit_method import PairsTradingFitMethod
|
||||
from pt_trading.results import BacktestResult
|
||||
from pt_trading.trading_pair import CointegrationData, TradingPair
|
||||
from pt_trading.trading_pair import CointegrationData, TradingPair, PairState
|
||||
|
||||
NanoPerMin = 1e9
|
||||
|
||||
@@ -72,7 +72,7 @@ class SlidingFit(PairsTradingFitMethod):
|
||||
curr_predicted_row_idx += 1
|
||||
|
||||
self._create_trading_signals(pair, config, bt_result)
|
||||
print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
|
||||
print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
|
||||
return pair.get_trades()
|
||||
|
||||
def _create_trading_signals(
|
||||
@@ -86,24 +86,41 @@ class SlidingFit(PairsTradingFitMethod):
|
||||
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, PairState.CLOSED_POSITIONS]:
|
||||
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
|
||||
scaled_disequilibrium = pred_row["scaled_disequilibrium"]
|
||||
|
||||
if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSE, PairState.CLOSE_POSITION]:
|
||||
if scaled_disequilibrium >= open_threshold:
|
||||
open_trades = self._get_open_trades(
|
||||
pair, row=pred_row, open_threshold=open_threshold
|
||||
)
|
||||
if open_trades is not None:
|
||||
open_trades["status"] = PairState.OPEN.name
|
||||
print(f"OPEN TRADES:\n{open_trades}")
|
||||
pair.add_trades(open_trades)
|
||||
pair.user_data_["state"] = PairState.OPEN
|
||||
pair.on_open_trades(open_trades)
|
||||
|
||||
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
|
||||
if scaled_disequilibrium <= close_threshold:
|
||||
close_trades = self._get_close_trades(
|
||||
pair, row=pred_row, close_threshold=close_threshold
|
||||
)
|
||||
if close_trades is not None:
|
||||
close_trades["status"] = PairState.CLOSE.name
|
||||
print(f"CLOSE TRADES:\n{close_trades}")
|
||||
pair.add_trades(close_trades)
|
||||
pair.user_data_["state"] = PairState.CLOSE
|
||||
pair.on_close_trades(close_trades)
|
||||
elif pair.to_stop_close_conditions(predicted_row=pred_row):
|
||||
close_trades = self._get_close_trades(
|
||||
pair, row=pred_row, close_threshold=close_threshold
|
||||
)
|
||||
if close_trades is not None:
|
||||
close_trades["status"] = pair.user_data_["stop_close_state"].name
|
||||
print(f"STOP CLOSE TRADES:\n{close_trades}")
|
||||
pair.add_trades(close_trades)
|
||||
pair.user_data_["state"] = pair.user_data_["stop_close_state"]
|
||||
pair.on_close_trades(close_trades)
|
||||
|
||||
# Outstanding positions
|
||||
if pair.user_data_["state"] == PairState.OPEN:
|
||||
@@ -112,16 +129,17 @@ class SlidingFit(PairsTradingFitMethod):
|
||||
)
|
||||
# outstanding positions
|
||||
if config["close_outstanding_positions"]:
|
||||
close_position_trades = self._get_close_position_trades(
|
||||
close_position_trades = self._get_close_trades(
|
||||
pair=pair,
|
||||
row=pred_row,
|
||||
close_threshold=close_threshold,
|
||||
)
|
||||
if close_position_trades is not None:
|
||||
close_position_trades["status"] = "CLOSE_POSITION"
|
||||
close_position_trades["status"] = PairState.CLOSE_POSITION.name
|
||||
print(f"CLOSE_POSITION TRADES:\n{close_position_trades}")
|
||||
pair.add_trades(close_position_trades)
|
||||
pair.user_data_["state"] = PairState.CLOSED_POSITIONS
|
||||
pair.user_data_["state"] = PairState.CLOSE_POSITION
|
||||
pair.on_close_trades(close_position_trades)
|
||||
else:
|
||||
if pair.predicted_df_ is not None:
|
||||
bt_result.handle_outstanding_position(
|
||||
@@ -154,9 +172,6 @@ class SlidingFit(PairsTradingFitMethod):
|
||||
open_px_a = open_row[f"{colname_a}"]
|
||||
open_px_b = open_row[f"{colname_b}"]
|
||||
|
||||
if open_scaled_disequilibrium < open_threshold:
|
||||
return None
|
||||
|
||||
# creating the trades
|
||||
print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}")
|
||||
if open_disequilibrium > 0:
|
||||
@@ -237,8 +252,6 @@ class SlidingFit(PairsTradingFitMethod):
|
||||
close_side_a = pair.user_data_["close_side_a"]
|
||||
close_side_b = pair.user_data_["close_side_b"]
|
||||
|
||||
if close_scaled_disequilibrium > close_threshold:
|
||||
return None
|
||||
trd_signal_tuples = [
|
||||
(
|
||||
close_tstamp,
|
||||
@@ -276,61 +289,114 @@ class SlidingFit(PairsTradingFitMethod):
|
||||
"pair": "object"
|
||||
})
|
||||
|
||||
def _get_close_position_trades(
|
||||
self, pair: TradingPair, row: pd.Series, close_threshold: float
|
||||
) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.colnames()
|
||||
# def _get_stop_close_trades(
|
||||
# self, pair: TradingPair, row: pd.Series, close_threshold: float
|
||||
# ) -> Optional[pd.DataFrame]:
|
||||
# colname_a, colname_b = pair.colnames()
|
||||
# assert pair.predicted_df_ is not None
|
||||
# if len(pair.predicted_df_) == 0:
|
||||
# return None
|
||||
|
||||
# stop_close_row = row
|
||||
# stop_close_tstamp = stop_close_row["tstamp"]
|
||||
# stop_close_disequilibrium = stop_close_row["disequilibrium"]
|
||||
# stop_close_scaled_disequilibrium = stop_close_row["scaled_disequilibrium"]
|
||||
# stop_close_px_a = stop_close_row[f"{colname_a}"]
|
||||
# stop_close_px_b = stop_close_row[f"{colname_b}"]
|
||||
|
||||
# stop_close_side_a = pair.user_data_["close_side_a"]
|
||||
# stop_close_side_b = pair.user_data_["close_side_b"]
|
||||
|
||||
# trd_signal_tuples = [
|
||||
# (
|
||||
# stop_close_tstamp,
|
||||
# stop_close_side_a,
|
||||
# pair.symbol_a_,
|
||||
# stop_close_px_a,
|
||||
# stop_close_disequilibrium,
|
||||
# stop_close_scaled_disequilibrium,
|
||||
# pair,
|
||||
# ),
|
||||
# (
|
||||
# stop_close_tstamp,
|
||||
# stop_close_side_b,
|
||||
# pair.symbol_b_,
|
||||
# stop_close_px_b,
|
||||
# stop_close_disequilibrium,
|
||||
# stop_close_scaled_disequilibrium,
|
||||
# pair,
|
||||
# ),
|
||||
# ]
|
||||
# df = pd.DataFrame(
|
||||
# trd_signal_tuples,
|
||||
# columns=self.TRADES_COLUMNS,
|
||||
# )
|
||||
# # Ensure consistent dtypes
|
||||
# return df.astype({
|
||||
# "time": "datetime64[ns]",
|
||||
# "action": "string",
|
||||
# "symbol": "string",
|
||||
# "price": "float64",
|
||||
# "disequilibrium": "float64",
|
||||
# "scaled_disequilibrium": "float64",
|
||||
# "pair": "object"
|
||||
# })
|
||||
|
||||
assert pair.predicted_df_ is not None
|
||||
if len(pair.predicted_df_) == 0:
|
||||
return None
|
||||
# def _get_close_position_trades(
|
||||
# self, pair: TradingPair, row: pd.Series, close_threshold: float
|
||||
# ) -> Optional[pd.DataFrame]:
|
||||
# colname_a, colname_b = pair.colnames()
|
||||
|
||||
close_position_row = row
|
||||
close_position_tstamp = close_position_row["tstamp"]
|
||||
close_position_disequilibrium = close_position_row["disequilibrium"]
|
||||
close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
|
||||
close_position_px_a = close_position_row[f"{colname_a}"]
|
||||
close_position_px_b = close_position_row[f"{colname_b}"]
|
||||
# assert pair.predicted_df_ is not None
|
||||
# if len(pair.predicted_df_) == 0:
|
||||
# return None
|
||||
|
||||
close_position_side_a = pair.user_data_["close_side_a"]
|
||||
close_position_side_b = pair.user_data_["close_side_b"]
|
||||
# close_position_row = row
|
||||
# close_position_tstamp = close_position_row["tstamp"]
|
||||
# close_position_disequilibrium = close_position_row["disequilibrium"]
|
||||
# close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
|
||||
# close_position_px_a = close_position_row[f"{colname_a}"]
|
||||
# close_position_px_b = close_position_row[f"{colname_b}"]
|
||||
|
||||
trd_signal_tuples = [
|
||||
(
|
||||
close_position_tstamp,
|
||||
close_position_side_a,
|
||||
pair.symbol_a_,
|
||||
close_position_px_a,
|
||||
close_position_disequilibrium,
|
||||
close_position_scaled_disequilibrium,
|
||||
pair,
|
||||
),
|
||||
(
|
||||
close_position_tstamp,
|
||||
close_position_side_b,
|
||||
pair.symbol_b_,
|
||||
close_position_px_b,
|
||||
close_position_disequilibrium,
|
||||
close_position_scaled_disequilibrium,
|
||||
pair,
|
||||
),
|
||||
]
|
||||
# close_position_side_a = pair.user_data_["close_side_a"]
|
||||
# close_position_side_b = pair.user_data_["close_side_b"]
|
||||
|
||||
# Add tuples to data frame with explicit dtypes to avoid concatenation warnings
|
||||
df = pd.DataFrame(
|
||||
trd_signal_tuples,
|
||||
columns=self.TRADES_COLUMNS,
|
||||
)
|
||||
# Ensure consistent dtypes
|
||||
return df.astype({
|
||||
"time": "datetime64[ns]",
|
||||
"action": "string",
|
||||
"symbol": "string",
|
||||
"price": "float64",
|
||||
"disequilibrium": "float64",
|
||||
"scaled_disequilibrium": "float64",
|
||||
"pair": "object"
|
||||
})
|
||||
# trd_signal_tuples = [
|
||||
# (
|
||||
# close_position_tstamp,
|
||||
# close_position_side_a,
|
||||
# pair.symbol_a_,
|
||||
# close_position_px_a,
|
||||
# close_position_disequilibrium,
|
||||
# close_position_scaled_disequilibrium,
|
||||
# pair,
|
||||
# ),
|
||||
# (
|
||||
# close_position_tstamp,
|
||||
# close_position_side_b,
|
||||
# pair.symbol_b_,
|
||||
# close_position_px_b,
|
||||
# close_position_disequilibrium,
|
||||
# close_position_scaled_disequilibrium,
|
||||
# pair,
|
||||
# ),
|
||||
# ]
|
||||
|
||||
# # Add tuples to data frame with explicit dtypes to avoid concatenation warnings
|
||||
# df = pd.DataFrame(
|
||||
# trd_signal_tuples,
|
||||
# columns=self.TRADES_COLUMNS,
|
||||
# )
|
||||
# # Ensure consistent dtypes
|
||||
# return df.astype({
|
||||
# "time": "datetime64[ns]",
|
||||
# "action": "string",
|
||||
# "symbol": "string",
|
||||
# "price": "float64",
|
||||
# "disequilibrium": "float64",
|
||||
# "scaled_disequilibrium": "float64",
|
||||
# "pair": "object"
|
||||
# })
|
||||
|
||||
def reset(self) -> None:
|
||||
curr_training_start_idx = 0
|
||||
|
||||
Reference in New Issue
Block a user