mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 00:37:43 +08:00
Scott Sanderson
ef4f642e62
This patch lays the groundwork for a compute engine designed to facilitate construction of factor-based universe screening and portfolio allocation. It contains: A new module, `zipline.modelling`, containing entities that can be used to express computations as dependency graphs. Each node in such a graph is an instance of the base `Term` class, defined in `zipline.modelling.term`. Dependency graphs are executed by instances of `FFCEngine`, defined in `zipline.modelling.engine`. A new module, `zipline.data.ffc`, containing loaders and dataset definitions for inputs to the modelling API. New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`. These methods can only be called from `initialize`, and are used to inform the algorithm that each day it should compute the given terms. Computed factor results are made available through a new attribute of the `data` object in `before_trading_start` and `handle_data`. Computed filter results control which assets are available in the factor matrix on each day.
92 lines
2.4 KiB
Python
92 lines
2.4 KiB
Python
"""
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Technical Analysis Factors
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--------------------------
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"""
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from bottleneck import (
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nanargmax,
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nanmax,
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nanmean,
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nansum,
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)
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from numpy import (
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clip,
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diff,
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fmax,
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inf,
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isnan,
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NINF,
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)
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from numexpr import evaluate
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from zipline.data.equities import USEquityPricing
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from zipline.modelling.term import SingleInputMixin
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from zipline.utils.control_flow import ignore_nanwarnings
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from .factor import CustomFactor
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class RSI(CustomFactor, SingleInputMixin):
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"""
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Factor computing rolling relative-strength index on a DataSet.
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Default Input: USEquityPricing.close
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Default Window Length: 14
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"""
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window_length = 14
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inputs = (USEquityPricing.close,)
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def compute(self, today, assets, out, closes):
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diffs = diff(closes)
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ups = nanmean(clip(diffs, 0, inf), axis=0)
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downs = nanmean(clip(diffs, -inf, 0), axis=0)
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return evaluate(
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"100 - (100 / (1 + (ups / downs)))",
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locals_dict={'ups': ups, 'downs': downs},
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globals_dict={},
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out=out,
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)
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class SimpleMovingAverage(CustomFactor, SingleInputMixin):
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"""
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Factor computing moving averages on a DataSet.
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"""
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# numpy's nan functions throw warnings when passed an array containing only
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# nans, but they still returns the desired value (nan), so we ignore the
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# warning.
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ctx = ignore_nanwarnings()
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def compute(self, today, assets, out, data):
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out[:] = nanmean(data, axis=0)
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class WeightedAverageValue(CustomFactor):
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"""
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Helper for VWAP-like computations.
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"""
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def compute(self, today, assets, out, base, weight):
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out[:] = nansum(base * weight, axis=0) / nansum(weight, axis=0)
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class VWAP(WeightedAverageValue):
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"""
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Volume-weighted average price
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"""
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inputs = (USEquityPricing.close, USEquityPricing.volume)
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class MaxDrawdown(CustomFactor, SingleInputMixin):
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"""
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Max Drawdown over a window
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"""
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ctx = ignore_nanwarnings()
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def compute(self, today, assets, out, data):
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drawdowns = fmax.accumulate(data, axis=0) - data
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drawdowns[isnan(drawdowns)] = NINF
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drawdown_ends = nanargmax(drawdowns, axis=0)
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# TODO: Accelerate this loop in Cython or Numba.
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for i, end in enumerate(drawdown_ends):
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peak = nanmax(data[:end + 1, i])
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out[i] = (peak - data[end, i]) / data[end, i]
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