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from abc import ( |
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ABCMeta, |
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abstractmethod, |
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) |
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import json |
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import os |
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from bcolz import ctable |
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from datetime import datetime |
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import numpy as np |
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from numpy import float64 |
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from os.path import join |
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import pandas as pd |
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from pandas import read_csv |
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from six import with_metaclass |
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from zipline.finance.trading import TradingEnvironment |
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from zipline.utils import tradingcalendar |
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MINUTES_PER_DAY = 390 |
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_writer_env = TradingEnvironment() |
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METADATA_FILENAME = 'metadata.json' |
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def write_metadata(directory, first_trading_day): |
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metadata_path = os.path.join(directory, METADATA_FILENAME) |
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metadata = { |
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'first_trading_day': str(first_trading_day.date()) |
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} |
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with open(metadata_path, 'w') as fp: |
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json.dump(metadata, fp) |
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class BcolzMinuteBarWriter(with_metaclass(ABCMeta)): |
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""" |
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Class capable of writing minute OHLCV data to disk into bcolz format. |
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""" |
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@property |
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def first_trading_day(self): |
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return self._first_trading_day |
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@abstractmethod |
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def gen_frames(self, assets): |
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""" |
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Return an iterator of pairs of (asset_id, pd.dataframe). |
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""" |
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raise NotImplementedError() |
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def write(self, directory, assets, sid_path_func=None): |
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_iterator = self.gen_frames(assets) |
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return self._write_internal(directory, _iterator, |
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sid_path_func=sid_path_func) |
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@staticmethod |
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def full_minutes_for_days(env, dt1, dt2): |
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start_date = env.normalize_date(dt1) |
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end_date = env.normalize_date(dt2) |
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all_minutes = [] |
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for day in env.days_in_range(start_date, end_date): |
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minutes_in_day = pd.date_range( |
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start=pd.Timestamp( |
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datetime( |
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year=day.year, |
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month=day.month, |
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day=day.day, |
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hour=9, |
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minute=31), |
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tz='US/Eastern').tz_convert('UTC'), |
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periods=390, |
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freq="min" |
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) |
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all_minutes.append(minutes_in_day) |
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# flatten |
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return pd.DatetimeIndex( |
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np.concatenate(all_minutes), copy=False, tz='UTC' |
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) |
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def _write_internal(self, directory, iterator, sid_path_func=None): |
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first_trading_day = self.first_trading_day |
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write_metadata(directory, first_trading_day) |
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first_open = pd.Timestamp( |
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datetime( |
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year=first_trading_day.year, |
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month=first_trading_day.month, |
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day=first_trading_day.day, |
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hour=9, |
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minute=31 |
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), tz='US/Eastern').tz_convert('UTC') |
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for asset_id, df in iterator: |
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if sid_path_func is None: |
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path = join(directory, "{0}.bcolz".format(asset_id)) |
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else: |
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path = sid_path_func(directory, asset_id) |
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os.makedirs(path) |
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minutes = self.full_minutes_for_days(_writer_env, |
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first_open, df.index[-1]) |
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minutes_count = len(minutes) |
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dt_col = np.zeros(minutes_count, dtype=np.uint32) |
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open_col = np.zeros(minutes_count, dtype=np.uint32) |
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high_col = np.zeros(minutes_count, dtype=np.uint32) |
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low_col = np.zeros(minutes_count, dtype=np.uint32) |
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close_col = np.zeros(minutes_count, dtype=np.uint32) |
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vol_col = np.zeros(minutes_count, dtype=np.uint32) |
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for row in df.iterrows(): |
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dt = row[0] |
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idx = minutes.searchsorted(dt) |
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dt_col[idx] = dt.value / 1e9 |
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open_col[idx] = row[1].loc["open"] |
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high_col[idx] = row[1].loc["high"] |
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low_col[idx] = row[1].loc["low"] |
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close_col[idx] = row[1].loc["close"] |
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vol_col[idx] = row[1].loc["volume"] |
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ctable( |
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columns=[ |
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open_col, |
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high_col, |
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low_col, |
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close_col, |
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vol_col, |
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dt_col |
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], |
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names=[ |
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"open", |
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"high", |
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"low", |
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"close", |
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"volume", |
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"dt" |
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], |
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rootdir=path, |
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mode='w' |
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) |
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class MinuteBarWriterFromDataFrames(BcolzMinuteBarWriter): |
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_csv_dtypes = { |
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'open': float64, |
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'high': float64, |
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'low': float64, |
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'close': float64, |
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'volume': float64, |
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} |
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def __init__(self, first_trading_day): |
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self._first_trading_day = first_trading_day |
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def gen_frames(self, assets): |
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for asset in assets: |
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df = assets[asset] |
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yield asset, df.set_index("minute") |
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class MinuteBarWriterFromCSVs(BcolzMinuteBarWriter): |
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""" |
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BcolzMinuteBarWriter constructed from a map of CSVs to assets. |
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Parameters |
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---------- |
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asset_map: dict |
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A map from asset_id -> path to csv with data for that asset. |
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CSVs should have the following columns: |
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minute : datetime64 |
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open : float64 |
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high : float64 |
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low : float64 |
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close : float64 |
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volume : int64 |
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""" |
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_csv_dtypes = { |
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'open': float64, |
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'high': float64, |
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'low': float64, |
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'close': float64, |
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'volume': float64, |
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} |
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def __init__(self, asset_map, first_trading_day): |
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self._asset_map = asset_map |
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self._first_trading_day = first_trading_day |
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def gen_frames(self, assets): |
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""" |
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Read CSVs as DataFrames from our asset map. |
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""" |
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dtypes = self._csv_dtypes |
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for asset in assets: |
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path = self._asset_map.get(asset) |
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if path is None: |
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raise KeyError("No path supplied for asset %s" % asset) |
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df = read_csv(path, parse_dates=['minute'], dtype=dtypes) |
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df = df.set_index("minute").tz_localize("UTC") |
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yield asset, df |
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class BcolzMinuteBarReader(object): |
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def __init__(self, rootdir): |
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self.rootdir = rootdir |
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metadata = self._get_metadata() |
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self.first_trading_day = pd.Timestamp( |
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metadata['first_trading_day'], tz='UTC') |
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mask = tradingcalendar.trading_days.slice_indexer( |
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self.first_trading_day) |
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self.trading_days = tradingcalendar.trading_days[mask] |
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def _get_metadata(self): |
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with open(os.path.join(self.rootdir, METADATA_FILENAME)) as fp: |
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return json.load(fp) |
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quantopian /
zipline
