async def main(broker_account_id=None):
balance = 10000
amount = 20
journal = Journal(mode='debug')
traider = Traider(balance, journal, amount)
analysis = Analysis(journal)
stock = Stock(traider, journal)
client = ti.AsyncClient(TOKEN, use_sandbox=True)
data = await get_data(client, figi)
print('Amount of data:', len(data))
df = pd.DataFrame(data)
df.set_index('date', inplace=True)
traider.trade(df,
strategy=StrategyMACD_Day(loss_level=loss,
profit_level=profit,
macd_level=macd_level,
target_stability=target_stability))
stock.interval_trade(df[-1:])
analysis.score()
no_update = True
async with ti.Streaming(TOKEN,
receive_timeout=20,
reconnect_timeout=10,
heartbeat=20) as streaming:
await streaming.candle.subscribe(figi, ti.CandleResolution.min5)
async for event in streaming:
if event.event == 'candle':
pass
# print(event.payload.figi, event.payload.c, event.payload.interval, event.payload.h, event.payload.time)
if not (event.payload.time.strftime('%e-%m-%Y %H:%M')
== data[-1]['date']) and no_update:
no_update = False
data = await get_data(client, figi)
df = pd.DataFrame(data)
df.set_index('date', inplace=True)
traider.trade(df,
strategy=StrategyMACD_Day(
loss_level=loss,
profit_level=profit,
macd_level=macd_level,
target_stability=target_stability))
stock.interval_trade(df[-1:])
analysis.score()
elif event.payload.time.strftime(
'%e-%m-%Y %H:%M') == data[-1]['date']:
no_update = True
data_ = df[:i + 1]
# print(data_[-1:])
# print(talib.MACD(data_['Close']))
traider.trade(data_,
strategy=StrategyMACD_Day(
loss_level=loss,
profit_level=profit,
macd_level=macd_level,
target_stability=target_stability))
# traider.trade(data_, strategy=StartegyBase())
stock.interval_trade(data_[i:i + 1])
# print(journal.get_orders())
# print(journal.get_limits())
# journal.save_to_csv()
analysis.score('data/results.csv')
journal = None
traider = None
stock = None
analysis = None
def search_limits():
for profit in np.linspace(0.01, 0.05, num=5):
res = []
for loss in np.linspace(0.001, 0.01, num=10):
for macd_level in np.linspace(0.05, 0.5, num=10):
for target_stability in np.linspace(0, 6, num=6):
for i in range(data_limit_MACD, len(df)):
data_ = df[:i + 1]
