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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
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run_portfolio_reporting.py
+64 -36
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@@ -11,10 +11,14 @@ import quantstats as qs
from alphalens.utils import get_clean_factor_and_forward_returns
def fixed_weight(row: pd.Series, availability_row: pd.Series, allow_short: bool) -> pd.Series:
def fixed_weight(
row: pd.Series, availability_row: pd.Series, allow_short: bool
) -> pd.Series:
no_of_assets_available = availability_row.sum()
unit = 1 / no_of_assets_available
if allow_short:
def determine_pos(x):
if x == 0.0 or np.isnan(x):
return 0
@@ -22,18 +26,21 @@ def fixed_weight(row: pd.Series, availability_row: pd.Series, allow_short: bool)
return 1
else:
return -1
row = row.apply(determine_pos)
else:
row = row.apply(lambda x: 1 if x > 0.0 else 0)
return row * unit
def limit_weight(row: pd.Series) -> pd.Series:
if row.sum() > 1:
row = row / row.sum()
elif row.sum() < -1:
row = row * (1. / abs(row.sum()))
row = row * (1.0 / abs(row.sum()))
return row
def only_top_bottom_2(row: pd.Series) -> pd.Series:
row = row.copy()
row_sorted = row.sort_values()
@@ -41,33 +48,36 @@ def only_top_bottom_2(row: pd.Series) -> pd.Series:
top = row_sorted.iloc[-2:]
middle = row_sorted.iloc[2:-2]
for index, _ in middle.iteritems():
row[index] = 0.
row[index] = 0.0
if bottom.sum() > 0:
for index, _ in bottom.iteritems():
row[index] = 0.
row[index] = 0.0
else:
for index, _ in bottom.iteritems():
row[index] = -.025
row[index] = -0.025
if top.sum() < 0:
for index, _ in top.iteritems():
row[index] = 0.
row[index] = 0.0
else:
for index, _ in top.iteritems():
row[index] = .025
row[index] = 0.025
return row
def equal_weight(row: pd.Series, availability: pd.Series) -> pd.Series:
no_of_assets_available = availability.sum()
unit = 1 / no_of_assets_available
for index, _ in predictions.iteritems():
row[index] = 0. if availability[index] == 0 else unit
row[index] = 0.0 if availability[index] == 0 else unit
return row
def create_naive_portfolio_weights(predictions: pd.DataFrame, availability: pd.DataFrame, allow_short: bool) -> pd.DataFrame:
def create_naive_portfolio_weights(
predictions: pd.DataFrame, availability: pd.DataFrame, allow_short: bool
) -> pd.DataFrame:
weights = predictions.copy()
assert weights.shape[1] == availability.shape[1]
for index, row in weights.iterrows():
@@ -79,14 +89,20 @@ def create_naive_portfolio_weights(predictions: pd.DataFrame, availability: pd.D
weights.iloc[index] = row
return weights
def create_quantile_weights(predictions: pd.DataFrame, availability: pd.DataFrame, allow_short: bool) -> pd.DataFrame:
def create_quantile_weights(
predictions: pd.DataFrame, availability: pd.DataFrame, allow_short: bool
) -> pd.DataFrame:
weights = predictions.copy()
assert weights.shape[1] == availability.shape[1]
quantiles = weights.copy()
for column in weights.columns:
quantiles[column] = pd.qcut(weights[column], q=4, labels=False, duplicates='drop')
quantiles[column] = pd.qcut(
weights[column], q=4, labels=False, duplicates="drop"
)
for index, row in quantiles.iterrows():
def only_select_bottom_top(x):
if x == 0:
return -1
@@ -94,6 +110,7 @@ def create_quantile_weights(predictions: pd.DataFrame, availability: pd.DataFram
return 1
else:
return 0
row = row.apply(only_select_bottom_top)
no_of_nonzero_predictions = row[row != 0].count()
units = min(1 / no_of_nonzero_predictions, 0.25)
@@ -101,49 +118,59 @@ def create_quantile_weights(predictions: pd.DataFrame, availability: pd.DataFram
return weights
predictions = pd.read_csv('output/predictions.csv', index_col=0)
predictions = pd.read_csv("output/predictions.csv", index_col=0)
predictions.index = pd.DatetimeIndex(predictions.index)
predictions.columns = ['_'.join(col.replace("model_", "").split("_")[:2]) for col in predictions.columns]
predictions.columns = [
"_".join(col.replace("model_", "").split("_")[:2]) for col in predictions.columns
]
first_index = get_first_valid_return_index(predictions[predictions.columns[0]])
predictions = predictions.iloc[first_index:]
close = load_only_returns(data_collections['daily_crypto'], 'price')
close = load_only_returns(data_collections["daily_crypto"], "price")
close = close.iloc[first_index:]
close.columns = [col.replace("_returns", "") for col in close.columns]
close = close[predictions.columns]
close = close.filter(items = predictions.index, axis = 0)
close = close.filter(items=predictions.index, axis=0)
availability = close.applymap(lambda x: 0 if x == 0.0 or x == 0 or np.isnan(x) else 1)
def report_alphalens():
alpha_factors = predictions.copy()
alpha_factors.index = close.index
alpha_factors_long = pd.melt(alpha_factors.reset_index(), id_vars=['time'], value_vars=alpha_factors.columns).set_index(['time', 'variable'])
alpha_factors_long = pd.melt(
alpha_factors.reset_index(), id_vars=["time"], value_vars=alpha_factors.columns
).set_index(["time", "variable"])
factor_data = get_clean_factor_and_forward_returns(
alpha_factors_long,
close,
quantiles=4,
periods=(1, 2, 3, 4, 5, 6, 10),
filter_zscore=None)
periods=(1, 2, 3, 4, 5, 6, 10),
filter_zscore=None,
)
# create_full_tear_sheet(factor_data, long_short=True)
from matplotlib.backends.backend_pdf import PdfPages
mean_return_by_q_daily, std_err = alphalens.performance.mean_return_by_quantile(factor_data, by_date=True)
mean_return_by_q, std_err_by_q = alphalens.performance.mean_return_by_quantile(factor_data, by_group=False)
mean_return_by_q_daily, std_err = alphalens.performance.mean_return_by_quantile(
factor_data, by_date=True
)
mean_return_by_q, std_err_by_q = alphalens.performance.mean_return_by_quantile(
factor_data, by_group=False
)
plot1 = alphalens.plotting.plot_quantile_returns_bar(mean_return_by_q)
plot2 = alphalens.plotting.plot_quantile_returns_violin(mean_return_by_q_daily)
plot3 = alphalens.plotting.plot_cumulative_returns_by_quantile(mean_return_by_q_daily, period='D')
plot3 = alphalens.plotting.plot_cumulative_returns_by_quantile(
mean_return_by_q_daily, period="D"
)
with PdfPages('output/factors.pdf') as pdf:
with PdfPages("output/factors.pdf") as pdf:
pdf.savefig(plot1.figure)
pdf.savefig(plot2.figure)
pdf.savefig(plot3.figure)
def report_backtest() -> vbt.Portfolio:
weights = create_quantile_weights(predictions, availability, allow_short=True)
@@ -151,7 +178,7 @@ def report_backtest() -> vbt.Portfolio:
# rebalance every n days
# weights.iloc[np.arange(len(weights)) % 2 != 0] = np.nan
weights.to_csv('output/weights.csv')
weights.to_csv("output/weights.csv")
portfolio = vbt.Portfolio.from_orders(
close=close,
@@ -161,26 +188,27 @@ def report_backtest() -> vbt.Portfolio:
cash_sharing=True,
call_seq=CallSeqType.Auto,
group_by=True,
freq='1D',
freq="1D",
raise_reject=True,
fees=0.001, # assuming 0.1% fees (1.5x of FTX)
slippage= 0.002, # assuming 0.2% slippage (5x of avg. spread on FTX)
fees=0.001, # assuming 0.1% fees (1.5x of FTX)
slippage=0.002, # assuming 0.2% slippage (5x of avg. spread on FTX)
seed=1,
init_cash=1e5,
log=True
log=True,
)
qs.reports.full(portfolio.returns(), portfolio.benchmark_returns())
qs.reports.full(portfolio.returns(), portfolio.benchmark_returns())
qs.reports.html(portfolio.returns(), portfolio.benchmark_returns(), output='output/report.html')
qs.reports.html(
portfolio.returns(), portfolio.benchmark_returns(), output="output/report.html"
)
print(portfolio.stats())
return portfolio
portfolio = report_backtest()
# from pypfopt import risk_models
# from pypfopt import expected_returns
# from pypfopt import EfficientFrontier