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add turnover_denom in create_txn_tear_sheet and fix some small issues #618

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Merged
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Sep 30, 2019
Merged

add turnover_denom in create_txn_tear_sheet and fix some small issues #618

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b830357
Merge pull request #3 from quantopian/master
huaiweicheng Sep 23, 2019
85433bd
del useless params in full tear sheet. add and adjust return_fig
huaiweicheng Sep 23, 2019
870b022
add turnover_denom para in txn tear sheet
huaiweicheng Sep 23, 2019
ef516e9
add turnover_denom para in txn tear sheet
huaiweicheng Sep 23, 2019
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14 changes: 10 additions & 4 deletions pyfolio/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1323,7 +1323,7 @@ def plot_return_quantiles(returns, live_start_date=None, ax=None, **kwargs):
return ax


def plot_turnover(returns, transactions, positions,
def plot_turnover(returns, transactions, positions, turnover_denom='AGB',
legend_loc='best', ax=None, **kwargs):
"""
Plots turnover vs. date.
Expand All @@ -1345,6 +1345,9 @@ def plot_turnover(returns, transactions, positions,
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
turnover_denom : str, optional
Either AGB or portfolio_value, default AGB.
- See full explanation in txn.get_turnover.
legend_loc : matplotlib.loc, optional
The location of the legend on the plot.
ax : matplotlib.Axes, optional
Expand All @@ -1364,7 +1367,7 @@ def plot_turnover(returns, transactions, positions,
y_axis_formatter = FuncFormatter(utils.two_dec_places)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))

df_turnover = txn.get_turnover(positions, transactions)
df_turnover = txn.get_turnover(positions, transactions, turnover_denom)
df_turnover_by_month = df_turnover.resample("M").mean()
df_turnover.plot(color='steelblue', alpha=1.0, lw=0.5, ax=ax, **kwargs)
df_turnover_by_month.plot(
Expand Down Expand Up @@ -1517,7 +1520,7 @@ def plot_capacity_sweep(returns, transactions, market_data,
return ax


def plot_daily_turnover_hist(transactions, positions,
def plot_daily_turnover_hist(transactions, positions, turnover_denom='AGB',
ax=None, **kwargs):
"""
Plots a histogram of daily turnover rates.
Expand All @@ -1530,6 +1533,9 @@ def plot_daily_turnover_hist(transactions, positions,
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
turnover_denom : str, optional
Either AGB or portfolio_value, default AGB.
- See full explanation in txn.get_turnover.
ax : matplotlib.Axes, optional
Axes upon which to plot.
**kwargs, optional
Expand All @@ -1543,7 +1549,7 @@ def plot_daily_turnover_hist(transactions, positions,

if ax is None:
ax = plt.gca()
turnover = txn.get_turnover(positions, transactions)
turnover = txn.get_turnover(positions, transactions, turnover_denom)
sns.distplot(turnover, ax=ax, **kwargs)
ax.set_title('Distribution of daily turnover rates')
ax.set_xlabel('Turnover rate')
Expand Down
47 changes: 27 additions & 20 deletions pyfolio/tears.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,12 +66,6 @@ def create_full_tear_sheet(returns,
cone_std=(1.0, 1.5, 2.0),
bootstrap=False,
unadjusted_returns=None,
style_factors=None,
sectors=None,
caps=None,
shares_held=None,
volumes=None,
percentile=None,
turnover_denom='AGB',
set_context=True,
factor_returns=None,
Expand Down Expand Up @@ -402,6 +396,7 @@ def create_simple_tear_sheet(returns,
plotting.plot_turnover(returns,
transactions,
positions,
turnover_denom=turnover_denom,
ax=ax_turnover)

plotting.plot_txn_time_hist(transactions, ax=ax_txn_timings)
Expand Down Expand Up @@ -603,8 +598,8 @@ def create_returns_tear_sheet(returns, positions=None,
@plotting.customize
def create_position_tear_sheet(returns, positions,
show_and_plot_top_pos=2, hide_positions=False,
return_fig=False, sector_mappings=None,
transactions=None, estimate_intraday='infer'):
sector_mappings=None, transactions=None,
estimate_intraday='infer', return_fig=False):
"""
Generate a number of plots for analyzing a
strategy's positions and holdings.
Expand All @@ -627,8 +622,6 @@ def create_position_tear_sheet(returns, positions,
hide_positions : bool, optional
If True, will not output any symbol names.
Overrides show_and_plot_top_pos to 0 to suppress text output.
return_fig : boolean, optional
If True, returns the figure that was plotted on.
sector_mappings : dict or pd.Series, optional
Security identifier to sector mapping.
Security ids as keys, sectors as values.
Expand All @@ -638,6 +631,8 @@ def create_position_tear_sheet(returns, positions,
estimate_intraday: boolean or str, optional
Approximate returns for intraday strategies.
See description in create_full_tear_sheet.
return_fig : boolean, optional
If True, returns the figure that was plotted on.
"""

positions = utils.check_intraday(estimate_intraday, returns,
Expand Down Expand Up @@ -697,8 +692,8 @@ def create_position_tear_sheet(returns, positions,

@plotting.customize
def create_txn_tear_sheet(returns, positions, transactions,
unadjusted_returns=None, estimate_intraday='infer',
return_fig=False):
turnover_denom='AGB', unadjusted_returns=None,
estimate_intraday='infer', return_fig=False):
"""
Generate a number of plots for analyzing a strategy's transactions.

Expand All @@ -715,6 +710,9 @@ def create_txn_tear_sheet(returns, positions, transactions,
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in create_full_tear_sheet.
turnover_denom : str, optional
Either AGB or portfolio_value, default AGB.
- See full explanation in txn.get_turnover.
unadjusted_returns : pd.Series, optional
Daily unadjusted returns of the strategy, noncumulative.
Will plot additional swippage sweep analysis.
Expand Down Expand Up @@ -743,12 +741,15 @@ def create_txn_tear_sheet(returns, positions, transactions,
returns,
transactions,
positions,
turnover_denom=turnover_denom,
ax=ax_turnover)

plotting.plot_daily_volume(returns, transactions, ax=ax_daily_volume)

try:
plotting.plot_daily_turnover_hist(transactions, positions,
plotting.plot_daily_turnover_hist(transactions,
positions,
turnover_denom=turnover_denom,
ax=ax_turnover_hist)
except ValueError:
warnings.warn('Unable to generate turnover plot.', UserWarning)
Expand Down Expand Up @@ -956,7 +957,8 @@ def create_capacity_tear_sheet(returns, positions, transactions,
trade_daily_vol_limit=0.05,
last_n_days=utils.APPROX_BDAYS_PER_MONTH * 6,
days_to_liquidate_limit=1,
estimate_intraday='infer'):
estimate_intraday='infer',
return_fig=False):
"""
Generates a report detailing portfolio size constraints set by
least liquid tickers. Plots a "capacity sweep," a curve describing
Expand Down Expand Up @@ -993,6 +995,8 @@ def create_capacity_tear_sheet(returns, positions, transactions,
estimate_intraday: boolean or str, optional
Approximate returns for intraday strategies.
See description in create_full_tear_sheet.
return_fig : boolean, optional
If True, returns the figure that was plotted on.
"""

positions = utils.check_intraday(estimate_intraday, returns,
Expand Down Expand Up @@ -1046,14 +1050,17 @@ def create_capacity_tear_sheet(returns, positions, transactions,
llt[llt['max_pct_bar_consumed'] > trade_daily_vol_limit * 100])

bt_starting_capital = positions.iloc[0].sum() / (1 + returns.iloc[0])
_, ax_capacity_sweep = plt.subplots(figsize=(14, 6))
fig, ax_capacity_sweep = plt.subplots(figsize=(14, 6))
plotting.plot_capacity_sweep(returns, transactions, market_data,
bt_starting_capital,
min_pv=100000,
max_pv=300000000,
step_size=1000000,
ax=ax_capacity_sweep)

if return_fig:
return fig


@plotting.customize
def create_perf_attrib_tear_sheet(returns,
Expand All @@ -1062,8 +1069,8 @@ def create_perf_attrib_tear_sheet(returns,
factor_loadings,
transactions=None,
pos_in_dollars=True,
return_fig=False,
factor_partitions=FACTOR_PARTITIONS):
factor_partitions=FACTOR_PARTITIONS,
return_fig=False):
"""
Generate plots and tables for analyzing a strategy's performance.

Expand Down Expand Up @@ -1093,15 +1100,15 @@ def create_perf_attrib_tear_sheet(returns,
Flag indicating whether `positions` are in dollars or percentages
If True, positions are in dollars.

return_fig : boolean, optional
If True, returns the figure that was plotted on.

factor_partitions : dict
dict specifying how factors should be separated in factor returns
and risk exposures plots
- Example:
{'style': ['momentum', 'size', 'value', ...],
'sector': ['technology', 'materials', ... ]}

return_fig : boolean, optional
If True, returns the figure that was plotted on.
"""
portfolio_exposures, perf_attrib_data = perf_attrib.perf_attrib(
returns, positions, factor_returns, factor_loadings, transactions,
Expand Down