model.plot_strategy_leverage() # plot the leverage of the portfolio
model.plot_strategy_group_pnl_trades(
) # plot the individual trade P&Ls
model.plot_strategy_group_benchmark_pnl(
) # plot all the cumulative P&Ls of each component
model.plot_strategy_group_benchmark_pnl_ir(
) # plot all the IR of individual components
model.plot_strategy_group_leverage(
) # plot all the individual leverages
from finmarketpy.backtest import TradeAnalysis
ta = TradeAnalysis()
# create statistics for the model returns using both finmarketpy and pyfolio
ta.run_strategy_returns_stats(model, engine='finmarketpy')
# ta.run_strategy_returns_stats(model, engine='pyfolio')
# model.plot_strategy_group_benchmark_annualised_pnl()
# create a FX CTA strategy, then examine how P&L changes with different vol targeting
# and later transaction costs
if True:
strategy = TradingModelFXTrend_Example()
from finmarketpy.backtest import TradeAnalysis
ta = TradeAnalysis()
ta.run_strategy_returns_stats(model, engine='finmarketpy')
# which backtesting parameters to change
'signal_vol_adjust': False
}]
pretty_portfolio_names = \
['Vol target',
'No vol target']
parameter_type = 'vol target'
ta.run_arbitrary_sensitivity(
strategy,
parameter_list=parameter_list,
pretty_portfolio_names=pretty_portfolio_names,
parameter_type=parameter_type)
# now examine sensitivity to different transaction costs
tc = [0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2.0]
ta.run_tc_shock(strategy, tc=tc)
# how does P&L change on day of month
ta.run_day_of_month_analysis(strategy)
# create a FX CTA strategy then use TradeAnalysis (via pyfolio) to analyse returns
if True:
from finmarketpy.backtest import TradeAnalysis
model = TradingModelFXTrend_Example()
model.construct_strategy()
tradeanalysis = TradeAnalysis()
tradeanalysis.run_strategy_returns_stats(strategy)
model.construct_strategy()
model.plot_strategy_pnl() # plot the final strategy
model.plot_strategy_leverage() # plot the leverage of the portfolio
model.plot_strategy_group_pnl_trades() # plot the individual trade P&Ls
model.plot_strategy_group_benchmark_pnl() # plot all the cumulative P&Ls of each component
model.plot_strategy_group_benchmark_pnl_ir() # plot all the IR of individual components
model.plot_strategy_group_leverage() # plot all the individual leverages
from finmarketpy.backtest import TradeAnalysis
ta = TradeAnalysis()
# create statistics for the model returns using both finmarketpy and pyfolio
ta.run_strategy_returns_stats(model, engine='finmarketpy')
# ta.run_strategy_returns_stats(model, engine='pyfolio')
# model.plot_strategy_group_benchmark_annualised_pnl()
# create a FX CTA strategy, then examine how P&L changes with different vol targeting
# and later transaction costs
if True:
strategy = TradingModelFXTrend_Example()
from finmarketpy.backtest import TradeAnalysis
ta = TradeAnalysis()
ta.run_strategy_returns_stats(model, engine='finmarketpy')
# which backtesting parameters to change
from findatapy.timeseries import Calculations
from findatapy.util.loggermanager import LoggerManager
ta = TradeAnalysis()
calc = Calculations()
logger = LoggerManager().getLogger(__name__)
chart = Chart(engine='matplotlib')
market = Market(market_data_generator=MarketDataGenerator())
# Choose run_example = 0 for everything
# run_example = 1 - use PyFolio to analyse gold's return properties
run_example = 0
###### Use PyFolio to analyse gold's return properties
if run_example == 1 or run_example == 0:
md_request = MarketDataRequest(
start_date="01 Jan 1996", # start date
data_source='bloomberg', # use Bloomberg as data source
tickers=['Gold'],
fields=['close'], # which fields to download
vendor_tickers=['XAUUSD Curncy'], # ticker (Bloomberg)
vendor_fields=['PX_LAST'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
df = market.fetch_market(md_request)
ta.run_strategy_returns_stats(None, index=df, engine='pyfolio')
