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from zipline.errors import ( |
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InvalidBenchmarkAsset, |
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BenchmarkAssetNotAvailableTooEarly, |
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BenchmarkAssetNotAvailableTooLate |
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) |
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from zipline.data.us_equity_pricing import NoDataOnDate |
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class BenchmarkSource(object): |
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def __init__(self, benchmark_sid, env, trading_days, data_portal, |
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emission_rate="daily"): |
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self.benchmark_sid = benchmark_sid |
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self.env = env |
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self.trading_days = trading_days |
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self.emission_rate = emission_rate |
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self.data_portal = data_portal |
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if self.benchmark_sid: |
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self.benchmark_asset = self.env.asset_finder.retrieve_asset( |
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self.benchmark_sid) |
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self._validate_benchmark() |
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self.precalculated_series = \ |
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self._initialize_precalculated_series( |
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self.benchmark_sid, |
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self.env, |
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self.trading_days, |
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self.data_portal |
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) |
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def get_value(self, dt): |
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return self.precalculated_series.loc[dt] |
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def _validate_benchmark(self): |
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# check if this security has a stock dividend. if so, raise an |
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# error suggesting that the user pick a different asset to use |
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# as benchmark. |
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stock_dividends = \ |
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self.data_portal.get_stock_dividends(self.benchmark_sid, |
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self.trading_days) |
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if len(stock_dividends) > 0: |
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raise InvalidBenchmarkAsset( |
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sid=str(self.benchmark_sid), |
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dt=stock_dividends[0]["ex_date"] |
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) |
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if self.benchmark_asset.start_date > self.trading_days[0]: |
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# the asset started trading after the first simulation day |
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raise BenchmarkAssetNotAvailableTooEarly( |
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sid=str(self.benchmark_sid), |
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dt=self.trading_days[0], |
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start_dt=self.benchmark_asset.start_date |
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) |
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if self.benchmark_asset.end_date < self.trading_days[-1]: |
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# the asset stopped trading before the last simulation day |
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raise BenchmarkAssetNotAvailableTooLate( |
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sid=str(self.benchmark_sid), |
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dt=self.trading_days[0], |
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end_dt=self.benchmark_asset.end_date |
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) |
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def _initialize_precalculated_series(self, sid, env, trading_days, |
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data_portal): |
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""" |
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Internal method that precalculates the benchmark return series for |
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use in the simulation. |
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Parameters |
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---------- |
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sid: (int) Asset to use |
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env: TradingEnvironment |
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trading_days: pd.DateTimeIndex |
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data_portal: DataPortal |
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Notes |
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----- |
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If the benchmark asset started trading after the simulation start, |
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or finished trading before the simulation end, exceptions are raised. |
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If the benchmark asset started trading the same day as the simulation |
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start, the first available minute price on that day is used instead |
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of the previous close. |
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We use history to get an adjusted price history for each day's close, |
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as of the look-back date (the last day of the simulation). Prices are |
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fully adjusted for dividends, splits, and mergers. |
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Returns |
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------- |
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A pd.Series, indexed by trading day, whose values represent the % |
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change from close to close. |
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""" |
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if sid is None: |
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# get benchmark info from trading environment, which defaults to |
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# downloading data from Yahoo. |
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daily_series = \ |
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env.benchmark_returns[trading_days[0]:trading_days[-1]] |
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if self.emission_rate == "minute": |
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# we need to take the env's benchmark returns, which are daily, |
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# and resample them to minute |
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minutes = env.minutes_for_days_in_range( |
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start=trading_days[0], |
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end=trading_days[-1] |
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) |
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minute_series = daily_series.reindex( |
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index=minutes, |
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method="ffill" |
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) |
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return minute_series |
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else: |
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return daily_series |
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elif self.emission_rate == "minute": |
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minutes = env.minutes_for_days_in_range(self.trading_days[0], |
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self.trading_days[-1]) |
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benchmark_series = data_portal.get_history_window( |
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[sid], |
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minutes[-1], |
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bar_count=len(minutes) + 1, |
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frequency="1m", |
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field="price", |
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ffill=True |
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) |
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return benchmark_series.pct_change()[1:] |
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else: |
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try: |
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# get the window of close prices for benchmark_sid from the |
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# last trading day of the simulation, going up to one day |
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# before the simulation start day (so that we can get the % |
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# change on day 1) |
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benchmark_series = data_portal.get_history_window( |
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[sid], |
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trading_days[-1], |
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bar_count=len(trading_days) + 1, |
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frequency="1d", |
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field="price", |
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ffill=True |
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)[sid] |
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return benchmark_series.pct_change()[1:] |
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except NoDataOnDate: |
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# Attempt to handle case where stock data starts on first |
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# day, in this case use the open to close return. |
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benchmark_series = data_portal.get_history_window( |
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[sid], |
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trading_days[-1], |
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bar_count=len(trading_days), |
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frequency="1d", |
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field="price", |
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ffill=True |
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)[sid] |
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# get a minute history window of the first day |
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first_open = data_portal.get_spot_value( |
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sid, 'open', trading_days[0]) |
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first_close = data_portal.get_spot_value( |
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sid, 'close', trading_days[0]) |
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first_day_return = (first_close - first_open) / first_open |
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returns = benchmark_series.pct_change()[:] |
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returns[0] = first_day_return |
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return returns |
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quantopian /
zipline
