ML based Pairs Selection

arbitragelab/__init__.py

@@ -0,0 +1,7 @@
+"""
+ArbitrageLab helps portfolio managers and traders who want to leverage the power of statistical arbitrage by providing
+reproducible, interpretable, and easy to use tools.
+"""
+
+from arbitragelab.ml_based_pairs_selection import pairs_selector
+from arbitragelab.ml_based_pairs_selection import data_importer

arbitragelab/ml_based_pairs_selection/__init__.py

@@ -0,0 +1,7 @@
+"""
+This module implements the ML Based Pairs Selection Technique
+"""
+
+from arbitragelab.ml_based_pairs_selection.pairs_selector import PairsSelector
+
+from arbitragelab.ml_based_pairs_selection.data_importer import DataImporter

arbitragelab/ml_based_pairs_selection/data_importer.py

@@ -0,0 +1,121 @@
+"""
+This module is a user data helper wrapping various yahoo finance libraries
+"""
+
+import pandas as pd
+
+import yfinance as yf
+import yahoo_fin.stock_info as ys
+
+
+class DataImporter:
+    """
+    Wrapper class that imports data from yfinance and yahoo_fin.
+    """
+
+    @staticmethod
+    def get_sp500_tickers() -> list:
+        """
+        Gets all S&P 500 stock tickers.
+
+        :return tickers: (list) : list of tickers
+        """
+        tickers_sp500 = ys.tickers_sp500()
+
+        return tickers_sp500
+
+    @staticmethod
+    def get_dow_tickers() -> list:
+        """
+        Gets all DOW stock tickers.
+
+        :return tickers: (list) : list of tickers
+        """
+        tickers_dow = ys.tickers_dow()
+
+        return tickers_dow
+
+    @staticmethod
+    def remove_nuns(dataframe: pd.DataFrame, threshold: int = 100) -> pd.DataFrame:
+        """
+        Remove tickers with nulls in value over a threshold.
+
+        :param dataframe: (pd.DataFrame) : Asset price data
+        :param threshold: (int) : The number of null values allowed
+        :return dataframe: (pd.DataFrame) : Price Data without any null values.
+        """
+        null_sum_each_ticker = dataframe.isnull().sum()
+        tickers_under_threshold = \
+            null_sum_each_ticker[null_sum_each_ticker <= threshold].index
+        dataframe = dataframe[tickers_under_threshold]
+
+        return dataframe
+
+    @staticmethod
+    def get_price_data(tickers: list,
+                       start_date: str,
+                       end_date: str,
+                       interval: str = '5m') -> pd.DataFrame:
+        """
+        Get the price data with custom start and end date and interval.
+        For daily price, only keep the closing price.
+
+        :param tickers: (list) : List of tickers to download
+        :param start_date: (str) : Download start date string (YYYY-MM-DD)
+        :param end_date: (str) : Download end date string (YYYY-MM-DD)
+        :param interval: (str) : Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
+        :return price_data: (pd.DataFrame) : The requested price_data.
+        """
+        price_data = yf.download(tickers,
+                                 start=start_date, end=end_date,
+                                 interval=interval,
+                                 group_by='column')['Close']
+
+        return price_data
+
+    @staticmethod
+    def get_returns_data(price_data: pd.DataFrame) -> pd.DataFrame:
+        """
+        Calculate return data with custom start and end date and interval.
+
+        :param price_data: (pd.DataFrame) : Asset price data
+        :return returns_df: (pd.DataFrame) : Price Data converted to returns.
+        """
+        returns_data = price_data.pct_change()
+        returns_data = returns_data.iloc[1:]
+
+        return returns_data
+
+    def get_ticker_sector_info(self, tickers: list, yf_call_chunk: int = 20) -> pd.DataFrame:
+        """
+        This method will loop through all the tickers, using the yfinance library
+        do a ticker info request and retrieve back 'sector' and 'industry' information.
+
+        :param tickers: (list) : List of asset symbols
+        :param yf_call_chunk: (int) : Ticker values allowed per 'Tickers' object. This should always be less than 200.
+        :return augmented_tickers: (pd.DataFrame) : DataFrame with input asset tickers and their respective sector and industry information
+        """
+
+        if len(tickers) > yf_call_chunk:
+            ticker_sector_queue = []
+            for i in range(0, len(tickers), yf_call_chunk):
+                end = i+yf_call_chunk if i <= len(tickers) else len(tickers)
+                ticker_sector_queue.append(
+                    self.get_ticker_sector_info(tickers[i: end]))
+            return pd.concat(ticker_sector_queue, axis=0).reset_index(drop=True)
+
+        tckrs = yf.Tickers(' '.join(tickers))
+
+        tckr_info = []
+
+        for i, tckr in enumerate(tickers):
+            try:
+                ticker_info = tckrs.tickers[i].info
+                tckr_info.append(
+                    (tckr, ticker_info['industry'], ticker_info['sector']))
+            except ValueError:  # pragma: no cover # can only happen if the server sends back corrupted data
+                pass
+            except RuntimeError:  # pragma: no cover # can only happen if server is down
+                pass
+
+        return pd.DataFrame(data=tckr_info, columns=['ticker', 'industry', 'sector'])