151 lines
5.7 KiB
Python
151 lines
5.7 KiB
Python
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from statsmodels.tsa.vector_ar.vecm import VECM
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class TradingPair:
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market_data_: pd.DataFrame
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symbol_a_: str
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symbol_b_: str
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price_column_: str
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training_mu_: Optional[float]
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training_std_: Optional[float]
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training_df_: Optional[pd.DataFrame]
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testing_df_: Optional[pd.DataFrame]
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vecm_fit_: Optional[VECM]
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user_data_: Dict[str, Any]
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def __init__(self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str):
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self.symbol_a_ = symbol_a
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self.symbol_b_ = symbol_b
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self.price_column_ = price_column
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self.market_data_ = self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
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self.training_mu_ = None
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self.training_std_ = None
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self.training_df_ = None
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self.testing_df_ = None
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self.vecm_fit_ = None
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self.user_data_ = {}
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def _transform_dataframe(self, df: pd.DataFrame):
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# Select only the columns we need
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df_selected = df[["tstamp", "symbol", self.price_column_]]
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# Start with unique timestamps
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result_df: pd.DataFrame = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
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# For each unique symbol, add a corresponding close price column
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for symbol in df_selected["symbol"].unique():
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# Filter rows for this symbol
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df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(drop=True)
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# Create column name like "close-COIN"
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new_price_column = f"{self.price_column_}_{symbol}"
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# Create temporary dataframe with timestamp and price
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temp_df = pd.DataFrame({
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"tstamp": df_symbol["tstamp"],
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new_price_column: df_symbol[self.price_column_]
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})
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# Join with our result dataframe
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result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
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result_df = result_df.reset_index(drop=True) # do not dropna() since irrelevant symbol would affect dataset
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return result_df
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def get_datasets(self, training_minutes: int, training_start_index: int = 0, testing_size: Optional[int] = None) -> None:
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testing_start_index = training_start_index + training_minutes
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self.training_df_ = self.market_data_.iloc[training_start_index:testing_start_index, :].copy()
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self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
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testing_start_index = training_start_index + training_minutes
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if testing_size is None:
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self.testing_df_ = self.market_data_.iloc[testing_start_index:, :].copy()
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else:
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self.testing_df_ = self.market_data_.iloc[testing_start_index:testing_start_index + testing_size, :].copy()
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self.testing_df_ = self.testing_df_.dropna().reset_index(drop=True)
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def colnames(self) -> List[str]:
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return [f"{self.price_column_}_{self.symbol_a_}", f"{self.price_column_}_{self.symbol_b_}"]
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def fit_VECM(self):
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vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
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vecm_model = VECM(vecm_df, coint_rank=1)
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vecm_fit = vecm_model.fit()
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# URGENT check beta and alpha
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# Check if the model converged properly
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if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
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print(f"{self}: VECM model failed to converge properly")
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self.vecm_fit_ = vecm_fit
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# print(f"{self}: beta={self.vecm_fit_.beta} alpha={self.vecm_fit_.alpha}" )
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# print(f"{self}: {self.vecm_fit_.summary()}")
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pass
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def check_cointegration(self):
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from statsmodels.tsa.vector_ar.vecm import coint_johansen
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df = self.training_df_[self.colnames()].reset_index(drop=True)
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result = coint_johansen(df, det_order=0, k_ar_diff=1)
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# print(f"{self}: lr1={result.lr1[0]} cvt={result.cvt[0, 1]}.")
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is_cointegrated = result.lr1[0] > result.cvt[0, 1]
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return is_cointegrated
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def train_pair(self) -> bool:
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is_cointegrated = self.check_cointegration()
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if not is_cointegrated:
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return False
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pass
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# print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
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self.fit_VECM()
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diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
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self.training_mu_ = diseq_series.mean().iloc[0]
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self.training_std_ = diseq_series.std().iloc[0]
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self.training_df_["dis-equilibrium"] = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
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# Normalize the dis-equilibrium
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self.training_df_["scaled_dis-equilibrium"] = (
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diseq_series - self.training_mu_
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) / self.training_std_
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return True
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def predict(self) -> None:
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predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
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# Convert prediction to a DataFrame for readability
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# predicted_df =
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self.predicted_df_ = pd.merge(
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self.testing_df_.reset_index(drop=True),
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pd.DataFrame(predicted_prices, columns=self.colnames()),
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left_index=True,
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right_index=True,
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suffixes=("", "_pred"),
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).dropna()
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self.predicted_df_["disequilibrium"] = self.predicted_df_[self.colnames()] @ self.vecm_fit_.beta
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self.predicted_df_["scaled_disequilibrium"] = (
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abs(self.predicted_df_["disequilibrium"] - self.training_mu_) / self.training_std_
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)
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# Reset index to ensure proper indexing
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self.predicted_df_ = self.predicted_df_.reset_index()
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return self.predicted_df_
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def __repr__(self) ->str:
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return f"{self.symbol_a_} & {self.symbol_b_}"
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