asset_df = ltsf.harvest_time_series(time_series_request)
spot_df = asset_df
logger.info("Running backtest...")
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, indicator, tech_params); signal_df = tech_ind.get_signal()
# use the same data for generating signals
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = cash_backtest.get_cumportfolio()
port.columns = [indicator + ' = ' + str(tech_params.sma_period) + ' ' + str(cash_backtest.get_portfolio_pnl_desc()[0])]
signals = cash_backtest.get_porfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Thalesians FX trend strategy"
gp.source = 'Thalesians/BBG (calc with PyThalesians Python library)'
gp.scale_factor = 1
gp.file_output = 'output_data/fx-trend-example.png'
pf.plot_line_graph(port, adapter = 'pythalesians', gp = gp)
###### backtest simple trend following strategy for FX spot basket
if True:
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
asset_df = ltsf.harvest_time_series(time_series_request)
spot_df = asset_df
logger.info("Running backtest...")
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, indicator, tech_params); signal_df = tech_ind.get_signal()
# use the same data for generating signals
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = cash_backtest.get_cumportfolio()
port.columns = [indicator + ' = ' + str(tech_params.sma_period) + ' ' + str(cash_backtest.get_portfolio_pnl_desc()[0])]
signals = cash_backtest.get_porfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Thalesians FX trend strategy"
gp.source = 'Thalesians/BBG (calc with PyThalesians Python library)'
gp.scale_factor = 1
gp.file_output = 'output_data/fx-trend-example.png'
pf.plot_line_graph(port, adapter = 'pythalesians', gp = gp)
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
asset_df = ltsf.harvest_time_series(time_series_request)
spot_df = asset_df
logger.info("Running backtest...")
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, indicator, tech_params); signal_df = tech_ind.get_signal()
# use the same data for generating signals
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = cash_backtest.get_cumportfolio()
port.columns = [indicator + ' = ' + str(tech_params.sma_period) + ' ' + str(cash_backtest.get_portfolio_pnl_desc()[0])]
signals = cash_backtest.get_porfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Thalesians FX trend strategy"
gp.source = 'Thalesians/BBG (calc with PyThalesians Python library)'
gp.scale_factor = 3
gp.file_output = 'output_data/FX-trend-example.png'
pf.plot_line_graph(port, adapter = 'pythalesians', gp = gp)
logger.info("Running backtest...")
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, indicator, tech_params)
signal_df = tech_ind.get_signal()
# use the same data for generating signals
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = cash_backtest.get_cumportfolio()
port.columns = [
indicator + ' = ' + str(tech_params.sma_period) + ' ' +
str(cash_backtest.get_portfolio_pnl_desc()[0])
]
signals = cash_backtest.get_porfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Thalesians FX trend strategy"
gp.source = 'Thalesians/BBG (calc with PyThalesians Python library)'
gp.scale_factor = 1
gp.file_output = 'output_data/fx-trend-example.png'
pf.plot_line_graph(port, adapter='pythalesians', gp=gp)
###### backtest simple trend following strategy for FX spot basket
if True:
