# create a FX trend strategy then chart the returns, leverage over time
if True:
model = TradingModelFXTrend_Example()
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()
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()
"""
Shows how to calculate returns of an asset
"""
# Loading data
import datetime
from chartpy import Chart, Style
from finmarketpy.backtest import TradeAnalysis
from findatapy.market import Market, MarketDataGenerator, MarketDataRequest
from chartpy.style import Style
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(
