def load_from_cli(
market_indicators,
machine_learning_models,
dataset,
columns,
data_indicators,
json_file,
python_script,
):
strat = Strategy()
if python_script is not None:
strat = get_strat(python_script)
columns = list(columns)
for i in market_indicators:
click.echo(f"Attaching indicator: {i.upper()}")
strat.add_market_indicator(i.upper())
for i in machine_learning_models:
click.echo(f"Attaching ml model: {i.upper()}")
strat.add_ml_models(i.upper())
# currently assigns -i indicator to the column provided at the same index
if dataset is not None:
click.echo(f"Using dataset: {dataset}")
strat.use_dataset(dataset, columns)
for i, ind in enumerate(data_indicators):
strat.add_data_indicator(dataset, ind.upper(), col=columns[i])
if json_file is not None:
click.echo(f"Loading from json file {json_file}")
strat.load_json_file(json_file)
click.secho(strat.serialize(), fg="white")
return strat
log.info("buying position cheaper than cost basis {} < {}".format(
context.price, context.cost_basis))
order(
asset=context.asset,
amount=context.buy_increment,
limit_price=context.price * (1 + context.SLIPPAGE_ALLOWED),
)
@strat.sell_order
def sell(context):
profit = (context.price * context.position.amount) - (
context.cost_basis * context.position.amount)
log.info("closing position, taking profit: {}".format(profit))
order_target_percent(asset=context.asset,
target=0,
limit_price=context.price *
(1 - context.SLIPPAGE_ALLOWED))
@strat.analyze()
def analyze(context, results, pos):
ending_cash = results.cash[-1]
log.info("Ending cash: ${}".format(ending_cash))
log.info("Completed for {} trading periods".format(context.i))
if __name__ == "__main__":
log.info("Strategy Schema:\n{}".format(strat.serialize()))
strat.run()
# Note that minute data is not supported for external datasets
# strat.trading_info['CAPITAL_BASE'] = 10000
# strat.trading_info['DATA_FREQ'] = 'minute'
# strat.trading_info['HISTORY_FREQ'] = '1m'
# strat.trading_info['START'] = '2017-12-10'
# strat.trading_info['END'] = '2017-12-11'
@strat.init
def init(context):
log.info('Algo is being initialzed, setting up context')
context.i = 0
@strat.handle_data
def handle_data(context, data):
log.debug('Processing new trading step')
context.i += 1
@strat.analyze()
def analyze(context, results, pos):
ending_cash = results.cash[-1]
log.info('Ending cash: ${}'.format(ending_cash))
log.info('Completed for {} trading periods'.format(context.i))
if __name__ == '__main__':
log.info('Strategy Schema:\n{}'.format(strat.serialize()))
strat.run()
def signal_sell(context, data):
return utils.cross_below(sma_fast.outputs.SMA_FAST,
sma_slow.outputs.SMA_SLOW)
@strat.signal_buy(override=True)
def signal_buy(context, data):
return utils.cross_above(sma_fast.outputs.SMA_FAST,
sma_slow.outputs.SMA_SLOW)
@strat.analyze(num_plots=1)
def extra_plot(context, results, pos):
viz.plot_column(results,
'SMA_FAST',
pos,
label='Fast',
y_label='Crossover')
viz.plot_column(results,
'SMA_SLOW',
pos,
label='Slow',
y_label='Crossover')
viz.plot_column(results, 'price', pos, y_label='price', linestyle="--")
plt.legend()
if __name__ == '__main__':
print('Strategy:\n{}'.format(strat.serialize()))
strat.run()