def bind_simulator(**sim_kwargs) -> Callable:
"""
Create a simulator that uses white noise for the preference matrix
"""
symbols: List[str] = data_io.get_all_symbols()
prices: pd.DataFrame = data_io.load_eod_matrix(symbols)
_bollinger: Callable = signals.create_bollinger_band_signal
bollinger_n = 20
returns = metrics.calculate_return_series(prices)
sharpe_n = 20
def bootstrap_rolling_sharpe_ratio(return_series: pd.Series) -> pd.Series:
_series = return_series.iloc[1:]
_series = _series.sample(n=return_series.shape[0], replace=True)
_series.iloc[:1] = [np.nan]
_series = pd.Series(_series.values, index=return_series.index)
_windowed_series = _series.rolling(sharpe_n)
return _windowed_series.mean() / _windowed_series.std()
_sharpe: Callable = bootstrap_rolling_sharpe_ratio
def _simulate(bootstrap_test_id: int) -> Performance:
signal = prices.apply(_bollinger, args=(bollinger_n,), axis=0)
preference = returns.apply(_sharpe, axis=0)
simulator = simulation.SimpleSimulator(**sim_kwargs)
simulator.simulate(prices, signal, preference)
return simulator.portfolio_history.get_performance_metric_data()
return _simulate
def bind_simulator(**sim_kwargs) -> Callable:
"""
Create a simulator that uses white noise for the preference matrix
"""
symbols: List[str] = data_io.get_all_symbols()
prices: pd.DataFrame = data_io.load_eod_matrix(symbols)
_bollinger: Callable = signals.create_bollinger_band_signal
# Bollinger n is constant throughout
bollinger_n = 20
def _simulate(white_noise_test_id: int) -> Performance:
signal = prices.apply(_bollinger, args=(bollinger_n, ), axis=0)
# Build a pile of noise in the same shape as the price data
_noise = np.random.normal(loc=0, scale=1, size=prices.shape)
_cols = prices.columns
_index = prices.index
preference = pd.DataFrame(_noise, columns=_cols, index=_index)
simulator = simulation.SimpleSimulator(**sim_kwargs)
simulator.simulate(prices, signal, preference)
return simulator.portfolio_history.get_performance_metric_data()
return _simulate
def simulate_portfolio():
bollinger_n = 20
sharpe_n = 100
# Load in data
symbols: List[str] = data_io.get_all_symbols()
prices: pd.DataFrame = data_io.load_eod_matrix(symbols)
# Use the Bollinger Band outer band crossover as a signal
_bollinger = signals.create_bollinger_band_signal
signal = prices.apply(_bollinger, args=(bollinger_n, ), axis=0)
# Use a rolling sharpe ratio approximation as a preference matrix
_sharpe = metrics.calculate_rolling_sharpe_ratio
preference = prices.apply(_sharpe, args=(sharpe_n, ), axis=0)
# Run the simulator
simulator = simulation.SimpleSimulator(
initial_cash=10000,
max_active_positions=5,
percent_slippage=0.0005,
trade_fee=1,
)
simulator.simulate(prices, signal, preference)
# Print results
simulator.portfolio_history.print_position_summaries()
simulator.print_initial_parameters()
simulator.portfolio_history.print_summary()
simulator.portfolio_history.plot()
simulator.portfolio_history.plot_benchmark_comparison()
def load_data() -> Tuple[List[str], pd.DataFrame, pd.DataFrame]:
"""
Load the data as is will be used in the alternative data model
"""
symbols: List[str] = data_io.get_all_symbols()
alt_data = data_io.load_alternative_data_matrix(symbols)
eod_data = data_io.load_eod_matrix(symbols)
eod_data = eod_data[eod_data.index >= alt_data.index.min()]
return symbols, eod_data, alt_data
def bind_simulator(**sim_kwargs) -> Callable:
"""
Create a function with all static simulation data bound to it, where the
arguments are simulation parameters
"""
symbols: List[str] = data_io.get_all_symbols()
prices: pd.DataFrame = data_io.load_eod_matrix(symbols)
_bollinger: Callable = signals.create_bollinger_band_signal
_sharpe: Callable = metrics.calculate_rolling_sharpe_ratio
def _simulate(bollinger_n: int, sharpe_n: int) -> Performance:
signal = prices.apply(_bollinger, args=(bollinger_n, ), axis=0)
preference = prices.apply(_sharpe, args=(sharpe_n, ), axis=0)
simulator = simulation.SimpleSimulator(**sim_kwargs)
simulator.simulate(prices, signal, preference)
return simulator.portfolio_history.get_performance_metric_data()
return _simulate
from pypm import data_io import numpy as np import pandas as pd from typing import List # Load in everything symbols: List[str] = data_io.get_all_symbols() eod_data: pd.DataFrame = data_io.load_eod_matrix(symbols) alt_data: pd.DataFrame = data_io.load_alternative_data_matrix(symbols) # Our eod_data goes back 10 years, but our alt_data goes back 5 years eod_data = eod_data[eod_data.index >= alt_data.index.min()] assert np.all(eod_data.index == alt_data.index) assert np.all(eod_data.columns == alt_data.columns)
