def compare_strategy_vs_benchmark(self, br, strategy_df, benchmark_df):
"""
compare_strategy_vs_benchmark - Compares the trading strategy we are backtesting against a benchmark
Parameters
----------
br : BacktestRequest
Parameters for backtest such as start and finish dates
strategy_df : pandas.DataFrame
Strategy time series
benchmark_df : pandas.DataFrame
Benchmark time series
"""
include_benchmark = False
calc_stats = False
if hasattr(br, 'include_benchmark'): include_benchmark = br.include_benchmark
if hasattr(br, 'calc_stats'): calc_stats = br.calc_stats
if include_benchmark:
ret_stats = RetStats()
risk_engine = RiskEngine()
filter = Filter()
calculations = Calculations()
# align strategy time series with that of benchmark
strategy_df, benchmark_df = strategy_df.align(benchmark_df, join='left', axis = 0)
# if necessary apply vol target to benchmark (to make it comparable with strategy)
if hasattr(br, 'portfolio_vol_adjust'):
if br.portfolio_vol_adjust is True:
benchmark_df = risk_engine.calculate_vol_adjusted_index_from_prices(benchmark_df, br = br)
# only calculate return statistics if this has been specified (note when different frequencies of data
# might underrepresent vol
# if calc_stats:
benchmark_df = benchmark_df.fillna(method='ffill')
ret_stats.calculate_ret_stats_from_prices(benchmark_df, br.ann_factor)
if calc_stats:
benchmark_df.columns = ret_stats.summary()
# realign strategy & benchmark
strategy_benchmark_df = strategy_df.join(benchmark_df, how='inner')
strategy_benchmark_df = strategy_benchmark_df.fillna(method='ffill')
strategy_benchmark_df = filter.filter_time_series_by_date(br.plot_start, br.finish_date, strategy_benchmark_df)
strategy_benchmark_df = calculations.create_mult_index_from_prices(strategy_benchmark_df)
self._benchmark_pnl = benchmark_df
self._benchmark_ret_stats = ret_stats
return strategy_benchmark_df
return strategy_df
def compare_strategy_vs_benchmark(self, br, strategy_df, benchmark_df):
"""Compares the trading strategy we are backtesting against a benchmark
Parameters
----------
br : BacktestRequest
Parameters for backtest such as start and finish dates
strategy_df : pandas.DataFrame
Strategy time series
benchmark_df : pandas.DataFrame
Benchmark time series
"""
include_benchmark = False
calc_stats = False
if hasattr(br, 'include_benchmark'): include_benchmark = br.include_benchmark
if hasattr(br, 'calc_stats'): calc_stats = br.calc_stats
if include_benchmark:
ret_stats = RetStats()
risk_engine = RiskEngine()
filter = Filter()
calculations = Calculations()
# align strategy time series with that of benchmark
strategy_df, benchmark_df = strategy_df.align(benchmark_df, join='left', axis = 0)
# if necessary apply vol target to benchmark (to make it comparable with strategy)
if hasattr(br, 'portfolio_vol_adjust'):
if br.portfolio_vol_adjust is True:
benchmark_df = risk_engine.calculate_vol_adjusted_index_from_prices(benchmark_df, br = br)
# only calculate return statistics if this has been specified (note when different frequencies of data
# might underrepresent vol
# if calc_stats:
benchmark_df = benchmark_df.fillna(method='ffill')
ret_stats.calculate_ret_stats_from_prices(benchmark_df, br.ann_factor)
if calc_stats:
benchmark_df.columns = ret_stats.summary()
# realign strategy & benchmark
strategy_benchmark_df = strategy_df.join(benchmark_df, how='inner')
strategy_benchmark_df = strategy_benchmark_df.fillna(method='ffill')
strategy_benchmark_df = filter.filter_time_series_by_date(br.plot_start, br.finish_date, strategy_benchmark_df)
strategy_benchmark_df = calculations.create_mult_index_from_prices(strategy_benchmark_df)
self._benchmark_pnl = benchmark_df
self._benchmark_ret_stats = ret_stats
return strategy_benchmark_df
return strategy_df
df_cuemacro_option_put_tc = fx_options_curve.apply_tc_to_total_return_index(cross, df_cuemacro_option_put_tot,
option_tc_bp=5, spot_tc_bp=2)
# Get total returns for spot
md_request.abstract_curve = None
# Get Bloomberg calculated total return indices (for spot)
md_request.category = 'fx-tot'
md_request.cut = 'NYC'
df_bbg_tot = market.fetch_market(md_request)
df_bbg_tot.columns = [x + '-bbg' for x in df_bbg_tot.columns]
calculations = Calculations()
chart.plot(calculations.create_mult_index_from_prices(
prepare_indices(df_tot=df_cuemacro_option_call_tot, df_tc=df_cuemacro_option_call_tc, df_spot_tot=df_bbg_tot)))
chart.plot(calculations.create_mult_index_from_prices(
prepare_indices(df_tot=df_cuemacro_option_put_tot, df_tc=df_cuemacro_option_put_tc, df_spot_tot=df_bbg_tot)))
chart.plot(calculations.create_mult_index_from_prices(
prepare_indices(df_tc=df_cuemacro_option_put_tc, df_spot_tot=df_bbg_tot)))
###### Fetch market data for pricing EURUSD options from 2006-2020 (ie. FX spot, FX forwards, FX deposits and FX vol quotes)
###### Construct volatility surface using FinancePy library underneath, using polynomial interpolation
###### Enters a short 1W straddle, and MTM every day, and at expiry rolls into another 1W straddle
if run_example == 2 or run_example == 0:
# Warning make sure you choose dates, where there is full vol surface! If vol points in the tenors you are looking at
# are missing then interpolation will fail (or if eg. spot data is missing etc.)
cache_algo='cache_algo_return',
abstract_curve=FXSpotCurve(construct_via_currency='USD', depo_tenor='ON'))
df_tot = market.fetch_market(md_request=md_request)
df_tot.columns = [x + '-tot-cuemacro' for x in df_tot.columns]
# Get spot data
md_request.abstract_curve = None
df_spot = market.fetch_market(md_request=md_request)
df_spot.columns = [x + '-spot' for x in df_spot.columns]
# Get Bloomberg calculated total return indices (for spot)
md_request.category = 'fx-tot'
df_bbg_tot = market.fetch_market(md_request)
df_bbg_tot.columns = [x + '-bbg' for x in df_bbg_tot.columns]
# Get Bloomberg calculated total return indices (for 1M forwards rolled)
md_request.category = 'fx-tot-forwards'
df_bbg_tot_forwards = market.fetch_market(md_request)
df_bbg_tot_forwards.columns = [x + '-bbg' for x in df_bbg_tot_forwards.columns]
# Combine into a single data frame and plot, we note that the Cuemacro constructed indices track the Bloomberg
# indices relatively well (both from spot and 1M forwards). Also note the large difference with spot indices
# CAREFUL to fill down, before reindexing because 1M forwards indices are likely to have different publishing dates
df = calculations.pandas_outer_join([df_tot, df_bbg_tot, df_spot, df_bbg_tot_forwards]).fillna(method='ffill')
df = calculations.create_mult_index_from_prices(df)
chart.plot(df)
