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zipline yfinance import with different exchange calendars
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marius
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2e5c326b83
commit
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172
Zipline/free_data_zipline_bundles.py
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172
Zipline/free_data_zipline_bundles.py
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import os
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import shutil
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import warnings
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from datetime import datetime, timedelta
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import numpy as np
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import pandas as pd
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import pytz
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import yfinance as yf
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from zipline.data import bundles
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from zipline.data.bundles import register
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from zipline.data.bundles.csvdir import csvdir_equities
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from zipline.data.data_portal import DataPortal
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from zipline.utils.calendar_utils import get_calendar
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from zipline.utils.cli import maybe_show_progress
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# Disable pandas PerformanceWarnings and FutureWarnings
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warnings.filterwarnings("ignore", category=pd.errors.PerformanceWarning)
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warnings.filterwarnings("ignore", category=FutureWarning)
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def safe_float_to_uint32(value):
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"""Safely convert float to uint32, clamping values if necessary."""
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return np.uint32(max(0, min(value, np.iinfo(np.uint32).max)))
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def process_symbol_data(df, trading_days, missing_dates):
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"""
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Processes the given dataframe (df) for a specific symbol,
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adjusts the data, and fills missing dates.
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Parameters:
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df (pandas DataFrame): The input dataframe containing the symbol's data.
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trading_days (list): A list of trading days for the specified symbol.
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missing_dates (list): A list of missing dates for the specified symbol.
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Returns:
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pandas DataFrame: A processed dataframe with adjusted data and missing dates filled.
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The function first sets the column names of the dataframe. It then drops any rows with missing values.
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An 'adj_factor' is calculated by dividing the 'adj_close' by the 'close' column.
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The 'open', 'high', 'low', and 'close' columns are then multiplied by the 'adj_factor'.
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The 'adj_close' and 'adj_factor' columns are then dropped from the dataframe.
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The index of the dataframe is renamed to 'date'.
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The 'dividend' and 'split' columns are initialized with 0.0 and 1.0 respectively.
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The dataframe is then reindexed to match the trading_days list.
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The dataframe is filled forward (ffill) to fill any missing values.
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For each missing date in the missing_dates list, if it's not already in the dataframe, the previous date's values are copied to that missing date.
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The dataframe is then sorted by the index (date).
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The 'open', 'high', 'low', and 'close' columns are then rounded to the nearest integer and converted to uint32 data type.
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The 'volume' column is filled with 0 if it's missing and clipped to ensure it's a non-negative integer. It's then converted to uint32 data type.
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The processed dataframe is then returned.
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"""
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def main():
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symbols = ['AAPL', 'NFLX', 'NVDA', 'JPM', 'SPY',
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"GC=F", "SI=F", "CL=F", "ZW=F", "PL=F", "ZC=F", "ZS=F", "KC=F",
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"CC=F", "^GDAXI", "^GSPC"]
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START_DATE = "2010-01-04"
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END_DATE = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d") # Use yesterday's date
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print(f"Downloading data for {len(symbols)} symbols from {START_DATE} to {END_DATE}")
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stocks_df = yf.download(tickers=symbols, start=START_DATE, end=END_DATE, group_by='ticker')
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print("Data download completed")
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calendar = get_calendar('XNYS')
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start_session = pd.Timestamp(START_DATE)
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end_session = pd.Timestamp(END_DATE)
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# Use pandas date_range instead of calendar.sessions_window
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all_days = pd.date_range(start=start_session, end=end_session, freq='D')
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trading_days = [day for day in all_days if calendar.is_session(day)]
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print(f"Total NYSE trading days: {len(trading_days)}")
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base_dir = os.path.expanduser('~/.zipline/custom_data/daily')
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os.makedirs(base_dir, exist_ok=True)
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missing_dates = [pd.Timestamp('2011-01-03'), pd.Timestamp('2016-10-10'), pd.Timestamp('2016-11-11')]
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for symbol in symbols:
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if symbol in stocks_df.columns.get_level_values(0):
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df = process_symbol_data(stocks_df[symbol].copy(), trading_days, missing_dates)
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print(f"\nSymbol: {symbol}")
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print(f"Date range: {df.index.min()} to {df.index.max()}")
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print(f"Number of rows: {len(df)}")
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print(f"Unique dates: {df.index.nunique()}")
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print("Sample of data:")
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print(df.head())
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csv_file_path = os.path.join(base_dir, f'{symbol}.csv')
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df.to_csv(csv_file_path)
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print(f"CSV file saved: {csv_file_path}")
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print("\nAll CSV files have been created")
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bundle_path = os.path.expanduser("~/.zipline/data/custom_data")
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if os.path.exists(bundle_path):
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shutil.rmtree(bundle_path)
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print(f"Removed existing bundle data: {bundle_path}")
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CSV_FOLDER_PATH = os.path.expanduser("~/.zipline/custom_data")
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print("\nRegistering the bundle")
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register(
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'custom_data',
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csvdir_equities(['daily'], CSV_FOLDER_PATH),
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calendar_name='XNYS',
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)
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print("Ingesting the bundle")
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try:
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bundles.ingest('custom_data')
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print("Bundle ingestion completed successfully")
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except Exception as e:
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print(f"Error during bundle ingestion: {str(e)}")
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raise
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print("\nLoading the bundle")
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try:
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bundle = bundles.load('custom_data')
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print("Bundle loaded successfully")
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except Exception as e:
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print(f"Error loading bundle: {str(e)}")
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raise
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assets = bundle.asset_finder.retrieve_all(bundle.asset_finder.sids)
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print(f"Bundle contains {len(assets)} assets")
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print("\nCreating DataPortal for data verification")
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try:
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data_portal = DataPortal(
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asset_finder=bundle.asset_finder,
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trading_calendar=calendar,
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first_trading_day=bundle.equity_daily_bar_reader.first_trading_day,
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equity_daily_reader=bundle.equity_daily_bar_reader,
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adjustment_reader=bundle.adjustment_reader
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)
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print("DataPortal created successfully")
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except Exception as e:
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print(f"Error creating DataPortal: {str(e)}")
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raise
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print("\nRetrieving historical data")
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try:
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bar_count = len(trading_days)
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panel = data_portal.get_history_window(
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assets=assets,
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end_dt=end_session,
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bar_count=bar_count,
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frequency='1d',
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field='close',
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data_frequency='daily'
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)
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print("Historical data retrieved successfully")
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print(f"\nData retrieved for {len(assets)} assets")
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print(f"Date range: {panel.index[0]} to {panel.index[-1]}")
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print(f"Number of trading days: {len(panel)}")
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print("\nSample of retrieved data:")
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print(panel.head())
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except Exception as e:
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print(f"Error retrieving historical data: {str(e)}")
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raise
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print("\nScript completed successfully")
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if __name__ == "__main__":
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main()
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