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Merge remote-tracking branch 'origin/main' into 27-soft-constraint-mu…
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…ltipliers
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@kasperjo
kasperjo committed Dec 25, 2023
2 parents 4ae2488 + 8a00026 commit 9d24fe4
Showing 1 changed file with 61 additions and 200 deletions.
261 changes: 61 additions & 200 deletions experiments/taming.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,7 +208,6 @@ def generate_table(
turnover_limit_result: BacktestResult,
robust_result: BacktestResult,
) -> None:
# Table 1
df = pd.DataFrame(
index=[
"Equal weight",
Expand Down Expand Up @@ -267,8 +266,6 @@ def generate_per_year_tables(
turnover_limit_result: BacktestResult,
robust_result: BacktestResult,
) -> None:
# TODO: DRY

years = sorted(equal_weights_results.history.year.unique())

equal_subs = []
Expand All @@ -288,213 +285,77 @@ def generate_per_year_tables(
turnover_limit_subs.append(get_sub_result(turnover_limit_result, sub_index))
robust_subs.append(get_sub_result(robust_result, sub_index))

mean_df = pd.concat(
[
pd.Series(
[result.mean_return for result in equal_subs], index=years, name="Equal"
),
pd.Series(
[result.mean_return for result in basic_subs], index=years, name="Basic"
),
pd.Series(
[result.mean_return for result in weight_limited_subs],
index=years,
name="Weight-limited",
),
pd.Series(
[result.mean_return for result in leverage_limit_subs],
index=years,
name="Leverage-limited",
),
pd.Series(
[result.mean_return for result in turnover_limit_subs],
index=years,
name="Turnover-limited",
),
pd.Series(
[result.mean_return for result in robust_subs],
index=years,
name="Robust",
),
],
axis=1,
process_dataframes(
equal_subs,
basic_subs,
weight_limited_subs,
leverage_limit_subs,
turnover_limit_subs,
robust_subs,
years,
)

print(mean_df.to_latex(float_format=lambda x: rf"{100 * x:.1f}\%"))

volatility_df = pd.concat(
[
pd.Series(
[result.volatility for result in equal_subs], index=years, name="Equal"
),
pd.Series(
[result.volatility for result in basic_subs], index=years, name="Basic"
),
pd.Series(
[result.volatility for result in weight_limited_subs],
index=years,
name="Weight-limited",
),
pd.Series(
[result.volatility for result in leverage_limit_subs],
index=years,
name="Leverage-limited",
),
pd.Series(
[result.volatility for result in turnover_limit_subs],
index=years,
name="Turnover-limited",
),
pd.Series(
[result.volatility for result in robust_subs],
index=years,
name="Robust",
),
],
axis=1,
)

print(volatility_df.to_latex(float_format=lambda x: rf"{100 * x:.1f}\%"))

sharpe_df = pd.concat(
[
pd.Series(
[result.sharpe for result in equal_subs], index=years, name="Equal"
),
pd.Series(
[result.sharpe for result in basic_subs], index=years, name="Basic"
),
pd.Series(
[result.sharpe for result in weight_limited_subs],
index=years,
name="Weight-limited",
),
pd.Series(
[result.sharpe for result in leverage_limit_subs],
index=years,
name="Leverage-limited",
),
pd.Series(
[result.sharpe for result in turnover_limit_subs],
index=years,
name="Turnover-limited",
),
pd.Series(
[result.sharpe for result in robust_subs], index=years, name="Robust"
),
],
axis=1,
)
def create_dataframe(subsets, years, attr, names, formatter):
series_list = [
pd.Series([getattr(result, attr) for result in subset], index=years, name=name)
for subset, name in zip(subsets, names)
]
df = pd.concat(series_list, axis=1)
print(df.applymap(formatter).to_latex())


def process_dataframes(
equal_subs,
basic_subs,
weight_limited_subs,
leverage_limit_subs,
turnover_limit_subs,
robust_subs,
years,
):
subsets = [
equal_subs,
basic_subs,
weight_limited_subs,
leverage_limit_subs,
turnover_limit_subs,
robust_subs,
]
names = [
"Equal",
"Basic",
"Weight-limited",
"Leverage-limited",
"Turnover-limited",
"Robust",
]

print(sharpe_df.to_latex(float_format=lambda x: f"{x:.2f}"))

turnover_df = pd.concat(
[
pd.Series(
[result.turnover for result in equal_subs], index=years, name="Equal"
),
pd.Series(
[result.turnover for result in basic_subs], index=years, name="Basic"
),
pd.Series(
[result.turnover for result in weight_limited_subs],
index=years,
name="Weight-limited",
),
pd.Series(
[result.turnover for result in leverage_limit_subs],
index=years,
name="Leverage-limited",
),
pd.Series(
[result.turnover for result in turnover_limit_subs],
index=years,
name="Turnover-limited",
),
pd.Series(
[result.turnover for result in robust_subs], index=years, name="Robust"
),
],
axis=1,
# Mean
create_dataframe(
subsets, years, "mean_return", names, formatter=lambda x: rf"{100 * x:.1f}\%"
)

print(turnover_df.to_latex(float_format=lambda x: f"{x:.1f}"))

leverage_df = pd.concat(
[
pd.Series(
[result.max_leverage for result in equal_subs],
index=years,
name="Equal",
),
pd.Series(
[result.max_leverage for result in basic_subs],
index=years,
name="Basic",
),
pd.Series(
[result.max_leverage for result in weight_limited_subs],
index=years,
name="Weight-limited",
),
pd.Series(
[result.max_leverage for result in leverage_limit_subs],
index=years,
name="Leverage-limited",
),
pd.Series(
[result.max_leverage for result in turnover_limit_subs],
index=years,
name="Turnover-limited",
),
pd.Series(
[result.max_leverage for result in robust_subs],
index=years,
name="Robust",
),
],
axis=1,
# Volatility
create_dataframe(
subsets, years, "volatility", names, formatter=lambda x: rf"{100 * x:.1f}\%"
)

print(leverage_df.to_latex(float_format=lambda x: f"{x:.1f}"))

drawdown_df = pd.concat(
[
pd.Series(
[result.max_drawdown for result in equal_subs],
index=years,
name="Equal",
),
pd.Series(
[result.max_drawdown for result in basic_subs],
index=years,
name="Basic",
),
pd.Series(
[result.max_drawdown for result in weight_limited_subs],
index=years,
name="Weight-limited",
),
pd.Series(
[result.max_drawdown for result in leverage_limit_subs],
index=years,
name="Leverage-limited",
),
pd.Series(
[result.max_drawdown for result in turnover_limit_subs],
index=years,
name="Turnover-limited",
),
pd.Series(
[result.max_drawdown for result in robust_subs],
index=years,
name="Robust",
),
],
axis=1,
# Sharpe Ratio
create_dataframe(subsets, years, "sharpe", names, formatter=lambda x: f"{x:.2f}")

# Turnover
create_dataframe(subsets, years, "turnover", names, formatter=lambda x: f"{x:.1f}")

# Leverage
create_dataframe(
subsets, years, "max_leverage", names, formatter=lambda x: f"{x:.1f}"
)

print(drawdown_df.to_latex(float_format=lambda x: rf"{-100 * x:.1f}\%"))
# Drawdown
create_dataframe(
subsets, years, "max_drawdown", names, formatter=lambda x: rf"{-100 * x:.1f}\%"
)


def get_sub_result(result: BacktestResult, sub_index: pd.Series) -> BacktestResult:
Expand Down

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