mirror of
https://github.com/wassname/options_backtester.git
synced 2026-06-27 18:05:27 +08:00
Removed asset backtester
This commit is contained in:
Juan Pablo Amoroso
@@ -1,3 +0,0 @@
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from . import datahandler, charts
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from .backtester import Backtest
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from .portfolio import *
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@@ -1,121 +0,0 @@
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import pandas as pd
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import pyprind
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from .portfolio import Portfolio
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class Backtest:
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def __init__(self, schema, initial_capital=1_000_000):
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self.schema = schema
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self._portfolio = None
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self._data = None
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self.initial_capital = initial_capital
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@property
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def portfolio(self):
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return self._portfolio
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@portfolio.setter
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def portfolio(self, portfolio):
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assert isinstance(portfolio, Portfolio)
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self._portfolio = portfolio
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@property
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def data(self):
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return self._data
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@data.setter
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def data(self, data):
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self._data = data
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def run(self, periods=1, sma_days=None):
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"""Runs a backtest and returns a dataframe with the daily balance"""
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assert self._data is not None
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assert self._portfolio is not None
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self.current_capital = 0
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self.current_cash = self.initial_capital
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self.inventory = pd.DataFrame(columns=['symbol', 'cost', 'qty'])
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self.balance = pd.DataFrame()
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if sma_days:
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self._data.sma(sma_days)
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data_iterator = self._data.iter_dates()
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first_day = self._data['date'].min()
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last_day = self._data['date'].max()
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rebalancing_days = pd.date_range(first_day, last_day, freq=str(periods) +
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'BMS').to_pydatetime() if periods is not None else []
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bar = pyprind.ProgBar(data_iterator.ngroups, bar_char='█')
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self.balance = pd.DataFrame({
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'capital': self.current_cash,
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'cash': self.current_cash
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},
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index=[self._data.start_date - pd.Timedelta(1, unit='day')])
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for date, data in data_iterator:
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if date == first_day:
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self._rebalance_portfolio(data, sma_days)
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self._update_balance(date, data)
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if date in rebalancing_days:
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self._rebalance_portfolio(data, sma_days)
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bar.update()
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self.balance['% change'] = self.balance['capital'].pct_change()
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self.balance['accumulated return'] = (1.0 + self.balance['% change']).cumprod()
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return self.balance
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def _rebalance_portfolio(self, data, sma_days):
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"""Rebalances the portfolio so that the total money is allocated according to the given percentages"""
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money_total = self.current_cash + self.current_capital
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for asset in self._portfolio.assets:
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query = '{} == "{}"'.format(self.schema['symbol'], asset.symbol)
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asset_current = data.query(query)
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asset_price = asset_current[self.schema['adjClose']].values[0]
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if sma_days is not None:
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if asset_current['sma'].values[0] < asset_price:
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qty = (money_total * asset.percentage) // asset_price
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else:
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qty = 0
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else:
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qty = (money_total * asset.percentage) // asset_price
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inventory_entry = self.inventory.query(query)
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self.inventory.drop(inventory_entry.index, inplace=True)
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updated_asset = pd.Series([asset.symbol, asset_price, qty])
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updated_asset.index = self.inventory.columns
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self.inventory = self.inventory.append(updated_asset, ignore_index=True)
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# Update current cash
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invested_capital = sum(self.inventory['cost'] * self.inventory['qty'])
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self.current_cash = money_total - invested_capital
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def _update_balance(self, date, data):
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"""Updates self.balance for the given date"""
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costs = []
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for asset in self._portfolio.assets:
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query = '{} == "{}"'.format(self.schema['symbol'], asset.symbol)
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asset_current = data.query(query)
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inventory_asset = self.inventory.query(query)
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cost = asset_current[self.schema['adjClose']].values[0]
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qty = inventory_asset['qty'].values[0]
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costs.append(cost * qty)
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total_value = sum(costs)
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self.current_capital = total_value
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money_total = total_value + self.current_cash
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row = pd.Series({
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'total value': total_value,
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'cash': self.current_cash,
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'capital': money_total,
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}, name=date)
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self.balance = self.balance.append(row)
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@@ -1,78 +0,0 @@
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"""Generates charts from a portfolio report"""
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import altair as alt
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import pandas as pd
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def returns_chart(report):
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# Time interval selector
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time_interval = alt.selection(type='interval', encodings=['x'])
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# Area plot
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areas = alt.Chart().mark_area(opacity=0.7).encode(x='index:T',
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y=alt.Y('accumulated return:Q', axis=alt.Axis(format='%')))
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# Nearest point selector
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nearest = alt.selection(type='single', nearest=True, on='mouseover', fields=['index'], empty='none')
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points = areas.mark_point().encode(opacity=alt.condition(nearest, alt.value(1), alt.value(0)))
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# Transparent date selector
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selectors = alt.Chart().mark_point().encode(
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x='index:T',
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opacity=alt.value(0),
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).add_selection(nearest)
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text = areas.mark_text(
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align='left', dx=5,
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dy=-5).encode(text=alt.condition(nearest, 'accumulated return:Q', alt.value(' '), format='.2%'))
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layered = alt.layer(selectors,
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points,
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text,
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areas.encode(
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alt.X('index:T', axis=alt.Axis(title='date'), scale=alt.Scale(domain=time_interval))),
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width=700,
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height=350,
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title='Wealth over time')
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lower = areas.properties(width=700, height=70).add_selection(time_interval)
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return alt.vconcat(layered, lower, data=report.reset_index())
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def returns_histogram(report):
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bar = alt.Chart(report).mark_bar().encode(x=alt.X('% change:Q',
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bin=alt.BinParams(maxbins=100),
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axis=alt.Axis(format='%')),
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y='count():Q')
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return bar
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def monthly_returns_heatmap(report):
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resample = report.resample('M')['capital'].last()
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monthly_returns = resample.pct_change().reset_index()
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monthly_returns['capital'].iat[0] = resample.iloc[0] / report.iloc[0]['capital'] - 1
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monthly_returns.columns = ['date', 'capital']
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chart = alt.Chart(monthly_returns).mark_rect().encode(
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alt.X('year(date):O', title='Year'), alt.Y('month(date):O', title='Month'),
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alt.Color('mean(capital)', title='Return', scale=alt.Scale(scheme='redyellowgreen')),
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alt.Tooltip('mean(capital)', format='.2f')).properties(title='Monthly Returns')
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return chart
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def sma_graph(data):
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price_chart = alt.Chart(
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data,
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width=700,
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height=350,
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).mark_line().encode(x='date:T', y=alt.Y('adjClose:Q'), color='symbol:N', opacity=alt.value(0.3))
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sma_chart = alt.Chart(
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data,
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width=700,
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height=350,
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).mark_line(strokeDash=[1, 1]).encode(x='date:T', y=alt.Y('sma:Q'), color='symbol:N')
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return price_chart + sma_chart
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@@ -1,2 +0,0 @@
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from .schema import *
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from .historical_asset_data import HistoricalAssetData
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@@ -1,79 +0,0 @@
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import os
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from .schema import Schema
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import pandas as pd
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class HistoricalAssetData:
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"""Historical Asset Data container class."""
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def __init__(self, file, schema=None, **params):
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if schema:
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assert isinstance(schema, Schema)
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else:
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self.schema = HistoricalAssetData.default_schema()
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file_extension = os.path.splitext(file)[1]
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if file_extension == '.h5':
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self._data = pd.read_hdf(file, **params)
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elif file_extension == '.csv':
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params['parse_dates'] = [self.schema.date.mapping]
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self._data = pd.read_csv(file, **params)
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columns = self._data.columns
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assert all((col in columns for _key, col in self.schema))
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date_col = self.schema['date']
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self.start_date = self._data[date_col].min()
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self.end_date = self._data[date_col].max()
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def apply_filter(self, f):
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"""Apply Filter `f` to the data. Returns a `pd.DataFrame` with the filtered rows."""
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return self._data.query(f.query)
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def iter_dates(self):
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"""Returns `pd.DataFrameGroupBy` that groups contracts by date"""
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return self._data.groupby(self.schema['date'])
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def __getattr__(self, attr):
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"""Pass method invocation to `self._data`"""
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method = getattr(self._data, attr)
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if hasattr(method, '__call__'):
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def df_method(*args, **kwargs):
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return method(*args, **kwargs)
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return df_method
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else:
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return method
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def __getitem__(self, item):
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if isinstance(item, pd.Series):
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return self._data[item]
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else:
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key = self.schema[item]
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return self._data[key]
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def __setitem__(self, key, value):
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self._data[key] = value
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if key not in self.schema:
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self.schema.update({key: key})
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def __len__(self):
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return len(self._data)
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def __repr__(self):
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return self._data.__repr__()
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def default_schema():
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"""Returns default schema for Historical Asset Data"""
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schema = Schema.canonical()
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return schema
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def sma(self, months):
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sma = self._data.groupby('symbol').rolling(months)['adjClose'].mean()
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sma = sma.reset_index('symbol').sort_index()
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sma = sma.fillna(0)
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self._data['sma'] = sma['adjClose']
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self.schema.update({'sma': 'sma'})
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@@ -1,162 +0,0 @@
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class Schema:
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"""Data schema class.
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Used to run validations and provide uniform access to fields in the data set.
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"""
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columns = [
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"symbol", "date", "open", "close", "high", "low", "volume", "adjClose", "adjHigh", "adjLow", "adjOpen",
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"adjVolume", "divCash", "splitFactor"
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]
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def canonical():
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"""Builder method that returns a `Schema` with default mappings"""
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mappings = {key: key for key in Schema.columns}
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return Schema(mappings)
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def __init__(self, mappings):
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assert all((key in mappings for key in Schema.columns))
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self._mappings = mappings
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def update(self, mappings):
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"""Update schema according to given `mappings`"""
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self._mappings.update(mappings)
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return self
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def __contains__(self, key):
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"""Returns True if key is in schema"""
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return key in self._mappings.keys()
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def __getattr__(self, key):
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"""Returns Field object used to build Filters"""
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return Field(key, self._mappings[key])
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def __setitem__(self, key, value):
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self._mappings[key] = value
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def __getitem__(self, key):
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"""Returns mapping of given `key`"""
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return self._mappings[key]
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def __iter__(self):
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return iter(self._mappings.items())
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def __repr__(self):
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return "Schema({})".format([Field(k, m) for k, m in self._mappings.items()])
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def __eq__(self, other):
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return self._mappings == other._mappings
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class Field:
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"""Encapsulates data fields to build filters used by strategies"""
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__slots__ = ("name", "mapping")
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def __init__(self, name, mapping):
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self.name = name
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self.mapping = mapping
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def _create_filter(self, op, other):
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if isinstance(other, Field):
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query = Field._format_query(self.mapping, op, other.mapping)
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else:
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query = Field._format_query(self.mapping, op, other)
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return Filter(query)
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def _combine_fields(self, op, other, invert=False):
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if isinstance(other, Field):
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name = Field._format_query(self.name, op, other.name, invert)
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mapping = Field._format_query(self.mapping, op, other.mapping, invert)
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elif isinstance(other, (int, float)):
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name = Field._format_query(self.name, op, other, invert)
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mapping = Field._format_query(self.mapping, op, other, invert)
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else:
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raise TypeError
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return Field(name, mapping)
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def _format_query(left, op, right, invert=False):
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if invert:
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left, right = right, left
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query = "{left} {op} {right}".format(left=left, op=op, right=right)
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return query
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def __add__(self, value):
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return self._combine_fields("+", value)
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def __radd__(self, value):
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return self._combine_fields("+", value, invert=True)
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def __sub__(self, value):
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return self._combine_fields("-", value)
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def __rsub__(self, value):
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return self._combine_fields("-", value, invert=True)
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def __mul__(self, value):
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return self._combine_fields("*", value)
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def __rmul__(self, value):
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return self._combine_fields("*", value, invert=True)
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def __truediv__(self, value):
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return self._combine_fields("/", value)
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def __rtruediv__(self, value):
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return self._combine_fields("/", value, invert=True)
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def __lt__(self, value):
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return self._create_filter("<", value)
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def __le__(self, value):
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return self._create_filter("<=", value)
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def __gt__(self, value):
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return self._create_filter(">", value)
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def __ge__(self, value):
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return self._create_filter(">=", value)
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def __eq__(self, value):
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if isinstance(value, str):
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value = "'{}'".format(value)
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return self._create_filter("==", value)
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def __ne__(self, value):
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return self._create_filter("!=", value)
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def __repr__(self):
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return "Field(name='{}', mapping='{}')".format(self.name, self.mapping)
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class Filter:
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"""This class determines entry/exit conditions for strategies"""
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__slots__ = ("query")
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def __init__(self, query):
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self.query = query
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def __and__(self, other):
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"""Returns logical *and* between `self` and `other`"""
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assert isinstance(other, Filter)
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new_query = "({}) & ({})".format(self.query, other.query)
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return Filter(query=new_query)
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def __or__(self, other):
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"""Returns logical *or* between `self` and `other`"""
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assert isinstance(other, Filter)
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new_query = "(({}) | ({}))".format(self.query, other.query)
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return Filter(query=new_query)
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def __invert__(self):
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"""Negates filter"""
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return Filter("!({})".format(self.query))
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def __call__(self, data):
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"""Returns dataframe of filtered data"""
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return data.eval(self.query)
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def __repr__(self):
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return "Filter(query='{}')".format(self.query)
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,2 +0,0 @@
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from .portfolio import Portfolio
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from .asset import Asset
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@@ -1,8 +0,0 @@
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class Asset:
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"""Asset data class"""
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def __init__(self, symbol, percentage):
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self.symbol = symbol
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self.percentage = percentage
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def __repr__(self):
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return "Asset(symbol={}, percentage={})".format(self.symbol, self.percentage)
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@@ -1,28 +0,0 @@
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from .asset import Asset
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class Portfolio:
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def __init__(self):
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self.assets = []
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def add_asset(self, asset):
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"""Adds asset to the Portfolio"""
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assert isinstance(asset, Asset)
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self.assets.append(asset)
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return self
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def add_assets(self, assets):
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"""Adds assets to the Portfolio"""
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for asset in assets:
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self.add_asset(asset)
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return self
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def remove_asset(self, asset_number):
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"""Removes asset from the Portfolio"""
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self.assets.pop(asset_number)
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return self
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def clear_assets(self):
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"""Removes *all* assets from the Portfolio"""
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self.assets = []
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return self
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@@ -1,103 +0,0 @@
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||||
import numpy as np
|
||||
|
||||
from asset_backtester import Backtest, Portfolio, Asset
|
||||
|
||||
# We use Portfolio Visualizer (https://www.portfoliovisualizer.com/backtest-portfolio)
|
||||
# to find the actual return for the test porfolios.
|
||||
|
||||
|
||||
def test_ivy_portfolio(sample_datahandler):
|
||||
bt = run_backtest(sample_datahandler, ivy_portfolio())
|
||||
|
||||
balance = bt.balance[1:]
|
||||
tolerance = 0.0001
|
||||
assert np.allclose(balance['capital'], balance['cash'] + balance['total value'], rtol=tolerance)
|
||||
assert np.allclose(balance['capital'], bt.initial_capital * balance['accumulated return'], rtol=tolerance)
|
||||
|
||||
actual_return = 1.2041
|
||||
return_tolerance = 0.01
|
||||
assert np.isclose(balance['accumulated return'].iloc[-1], actual_return, rtol=return_tolerance)
|
||||
|
||||
|
||||
def test_ivy_monthly_rebalance(sample_datahandler):
|
||||
bt = run_backtest(sample_datahandler, ivy_portfolio(), periods=1)
|
||||
|
||||
balance = bt.balance[1:]
|
||||
tolerance = 0.0001
|
||||
assert np.allclose(balance['capital'], balance['cash'] + balance['total value'], rtol=tolerance)
|
||||
assert np.allclose(balance['capital'], bt.initial_capital * balance['accumulated return'], rtol=tolerance)
|
||||
|
||||
actual_return = 1.2043
|
||||
return_tolerance = 0.01
|
||||
assert np.isclose(balance['accumulated return'].iloc[-1], actual_return, rtol=return_tolerance)
|
||||
|
||||
|
||||
def test_all_weather_portfolio(sample_datahandler):
|
||||
bt = run_backtest(sample_datahandler, all_weather_portfolio())
|
||||
|
||||
balance = bt.balance[1:]
|
||||
tolerance = 0.0001
|
||||
assert np.allclose(balance['capital'], balance['cash'] + balance['total value'], rtol=tolerance)
|
||||
assert np.allclose(balance['capital'], bt.initial_capital * balance['accumulated return'], rtol=tolerance)
|
||||
|
||||
actual_return = 1.1874
|
||||
return_tolerance = 0.01
|
||||
assert np.isclose(balance['accumulated return'].iloc[-1], actual_return, rtol=return_tolerance)
|
||||
|
||||
|
||||
def test_all_weather_monthly_rebalance(sample_datahandler):
|
||||
bt = run_backtest(sample_datahandler, all_weather_portfolio(), periods=1)
|
||||
|
||||
balance = bt.balance[1:]
|
||||
tolerance = 0.0001
|
||||
assert np.allclose(balance['capital'], balance['cash'] + balance['total value'], rtol=tolerance)
|
||||
assert np.allclose(balance['capital'], bt.initial_capital * balance['accumulated return'], rtol=tolerance)
|
||||
|
||||
actual_return = 1.1828
|
||||
return_tolerance = 0.01
|
||||
assert np.isclose(balance['accumulated return'].iloc[-1], actual_return, rtol=return_tolerance)
|
||||
|
||||
|
||||
def test_constant_price(constant_price_datahandler):
|
||||
bt = run_backtest(constant_price_datahandler, ivy_portfolio())
|
||||
|
||||
balance = bt.balance[1:]
|
||||
tolerance = 0.0001
|
||||
assert np.allclose(balance['% change'], 0.0, rtol=tolerance)
|
||||
assert np.allclose(balance['capital'], bt.initial_capital, rtol=tolerance)
|
||||
assert np.allclose(balance['total value'], bt.initial_capital, rtol=tolerance)
|
||||
assert np.allclose(balance['accumulated return'], 1.0, rtol=tolerance)
|
||||
|
||||
|
||||
def test_zero_initial_capital(sample_datahandler):
|
||||
bt = run_backtest(sample_datahandler, ivy_portfolio(), initial_capital=0)
|
||||
|
||||
balance = bt.balance[1:]
|
||||
tolerance = 0.0001
|
||||
assert np.allclose(balance['capital'], balance['cash'] + balance['total value'], rtol=tolerance)
|
||||
assert np.allclose(balance['cash'], 0.0, rtol=tolerance)
|
||||
assert np.allclose(balance['total value'], 0.0, rtol=tolerance)
|
||||
|
||||
|
||||
# Helpers
|
||||
def run_backtest(data, portfolio, initial_capital=1_000_000, periods=None):
|
||||
bt = Backtest(data.schema, initial_capital=initial_capital)
|
||||
bt.portfolio = portfolio
|
||||
bt.data = data
|
||||
bt.run(periods=periods)
|
||||
|
||||
return bt
|
||||
|
||||
|
||||
def ivy_portfolio():
|
||||
portfolio = Portfolio()
|
||||
assets = [Asset('VTI', 0.2), Asset('VEU', 0.2), Asset('BND', 0.2), Asset('VNQ', 0.2), Asset('DBC', 0.2)]
|
||||
|
||||
return portfolio.add_assets(assets)
|
||||
|
||||
|
||||
def all_weather_portfolio():
|
||||
portfolio = Portfolio()
|
||||
assets = [Asset('VTI', 0.3), Asset('TLT', 0.4), Asset('IEF', 0.15), Asset('GLD', 0.075), Asset('DBC', 0.075)]
|
||||
|
||||
return portfolio.add_assets(assets)
|
||||
@@ -1,23 +0,0 @@
|
||||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
from asset_backtester.datahandler import HistoricalAssetData
|
||||
|
||||
TEST_DIR = os.path.abspath(os.path.dirname(__file__))
|
||||
|
||||
# 2019 data for TLT, GLD, IEF, VTI, VEU, BND, VNQ and DBC
|
||||
SAMPLE_DATA = os.path.join(TEST_DIR, 'test_data', 'sample_data.csv')
|
||||
|
||||
|
||||
@pytest.fixture(scope='module')
|
||||
def sample_datahandler():
|
||||
data = HistoricalAssetData(SAMPLE_DATA)
|
||||
return data
|
||||
|
||||
|
||||
@pytest.fixture(scope='module')
|
||||
def constant_price_datahandler():
|
||||
data = HistoricalAssetData(SAMPLE_DATA)
|
||||
data['adjClose'] = data['close'] = 10.0
|
||||
return data
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user