mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
197 lines
6.5 KiB
Python
197 lines
6.5 KiB
Python
#%%
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import pandas as pd
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import numpy as np
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from data_loader import load_only_returns, data_collections
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from utils.helpers import get_first_valid_return_index
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import alphalens
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import vectorbt as vbt
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from vectorbt.portfolio.enums import SizeType, CallSeqType, Direction
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import quantstats as qs
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from alphalens.utils import get_clean_factor_and_forward_returns
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def fixed_weight(row: pd.Series, availability_row: pd.Series, allow_short: bool) -> pd.Series:
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no_of_assets_available = availability_row.sum()
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unit = 1 / no_of_assets_available
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if allow_short:
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def determine_pos(x):
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if x == 0.0 or np.isnan(x):
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return 0
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elif x > 0.0:
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return 1
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else:
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return -1
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row = row.apply(determine_pos)
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else:
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row = row.apply(lambda x: 1 if x > 0.0 else 0)
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return row * unit
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def limit_weight(row: pd.Series) -> pd.Series:
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if row.sum() > 1:
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row = row / row.sum()
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elif row.sum() < -1:
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row = row * (1. / abs(row.sum()))
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return row
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def only_top_bottom_2(row: pd.Series) -> pd.Series:
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row = row.copy()
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row_sorted = row.sort_values()
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bottom = row_sorted.iloc[:2]
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top = row_sorted.iloc[-2:]
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middle = row_sorted.iloc[2:-2]
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for index, _ in middle.iteritems():
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row[index] = 0.
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if bottom.sum() > 0:
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for index, _ in bottom.iteritems():
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row[index] = 0.
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else:
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for index, _ in bottom.iteritems():
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row[index] = -.025
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if top.sum() < 0:
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for index, _ in top.iteritems():
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row[index] = 0.
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else:
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for index, _ in top.iteritems():
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row[index] = .025
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return row
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def equal_weight(row: pd.Series, availability: pd.Series) -> pd.Series:
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no_of_assets_available = availability.sum()
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unit = 1 / no_of_assets_available
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for index, _ in predictions.iteritems():
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row[index] = 0. if availability[index] == 0 else unit
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return row
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def create_naive_portfolio_weights(predictions: pd.DataFrame, availability: pd.DataFrame, allow_short: bool) -> pd.DataFrame:
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weights = predictions.copy()
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assert weights.shape[1] == availability.shape[1]
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for index, row in weights.iterrows():
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# row = fixed_weight(row, availability.iloc[index], allow_short)
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# row = row / row.sum()
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# row = only_top_bottom_2(row)
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# row = equal_weight(row, availability.iloc[index])
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row = limit_weight(row)
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weights.iloc[index] = row
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return weights
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def create_quantile_weights(predictions: pd.DataFrame, availability: pd.DataFrame, allow_short: bool) -> pd.DataFrame:
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weights = predictions.copy()
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assert weights.shape[1] == availability.shape[1]
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quantiles = weights.copy()
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for column in weights.columns:
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quantiles[column] = pd.qcut(weights[column], q=4, labels=False, duplicates='drop')
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for index, row in quantiles.iterrows():
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def only_select_bottom_top(x):
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if x == 0:
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return -1
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elif x == 3:
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return 1
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else:
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return 0
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row = row.apply(only_select_bottom_top)
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no_of_nonzero_predictions = row[row != 0].count()
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units = min(1 / no_of_nonzero_predictions, 0.25)
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weights.loc[index] = row * units
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return weights
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predictions = pd.read_csv('output/predictions.csv', index_col=0)
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predictions.index = pd.DatetimeIndex(predictions.index)
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predictions.columns = ['_'.join(col.replace("model_", "").split("_")[:2]) for col in predictions.columns]
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first_index = get_first_valid_return_index(predictions[predictions.columns[0]])
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predictions = predictions.iloc[first_index:]
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close = load_only_returns(data_collections['daily_crypto'], 'price')
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close = close.iloc[first_index:]
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close.columns = [col.replace("_returns", "") for col in close.columns]
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close = close[predictions.columns]
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close = close.filter(items = predictions.index, axis = 0)
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availability = close.applymap(lambda x: 0 if x == 0.0 or x == 0 or np.isnan(x) else 1)
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def report_alphalens():
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alpha_factors = predictions.copy()
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alpha_factors.index = close.index
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alpha_factors_long = pd.melt(alpha_factors.reset_index(), id_vars=['time'], value_vars=alpha_factors.columns).set_index(['time', 'variable'])
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factor_data = get_clean_factor_and_forward_returns(
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alpha_factors_long,
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close,
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quantiles=4,
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periods=(1, 2, 3, 4, 5, 6, 10),
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filter_zscore=None)
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# create_full_tear_sheet(factor_data, long_short=True)
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from matplotlib.backends.backend_pdf import PdfPages
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mean_return_by_q_daily, std_err = alphalens.performance.mean_return_by_quantile(factor_data, by_date=True)
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mean_return_by_q, std_err_by_q = alphalens.performance.mean_return_by_quantile(factor_data, by_group=False)
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plot1 = alphalens.plotting.plot_quantile_returns_bar(mean_return_by_q)
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plot2 = alphalens.plotting.plot_quantile_returns_violin(mean_return_by_q_daily)
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plot3 = alphalens.plotting.plot_cumulative_returns_by_quantile(mean_return_by_q_daily, period='D')
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with PdfPages('output/factors.pdf') as pdf:
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pdf.savefig(plot1.figure)
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pdf.savefig(plot2.figure)
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pdf.savefig(plot3.figure)
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def report_backtest() -> vbt.Portfolio:
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weights = create_quantile_weights(predictions, availability, allow_short=True)
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weights.index = close.index
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# rebalance every n days
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# weights.iloc[np.arange(len(weights)) % 2 != 0] = np.nan
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weights.to_csv('output/weights.csv')
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portfolio = vbt.Portfolio.from_orders(
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close=close,
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size=weights,
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size_type=SizeType.TargetPercent,
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direction=Direction.Both,
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cash_sharing=True,
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call_seq=CallSeqType.Auto,
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group_by=True,
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freq='1D',
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raise_reject=True,
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fees=0.001, # assuming 0.1% fees (1.5x of FTX)
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slippage= 0.002, # assuming 0.2% slippage (5x of avg. spread on FTX)
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seed=1,
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init_cash=1e5,
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log=True
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)
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qs.reports.full(portfolio.returns(), portfolio.benchmark_returns())
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qs.reports.html(portfolio.returns(), portfolio.benchmark_returns(), output='output/report.html')
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print(portfolio.stats())
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return portfolio
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portfolio = report_backtest()
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# from pypfopt import risk_models
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# from pypfopt import expected_returns
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# from pypfopt import EfficientFrontier
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# mu = expected_returns.mean_historical_return(close)
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# S = risk_models.sample_cov(close)
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# ef = EfficientFrontier(mu, S)
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# raw_weights = ef.max_sharpe()
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# cleaned_weights = ef.clean_weights()
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# print(ef.portfolio_performance(verbose=True))
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# %%
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