if results == True:
all_results = pd.concat([
all_results,
get_results(prices, max_min, pat, stock, ema_, window_)
],
axis=0)
if results == True:
return all_results.reset_index(drop=True)
if __name__ == '__main__':
# Get data from database
allMinutePriceData = model.getMinutePriceData(start=datetime(2020, 11, 25),
end=datetime(2020, 12, 1),
stockSymbol='INFY')
resampled_data = allMinutePriceData
resampled_data = resampled_data.rename(columns={'datetime': 'timestamp'})
resampled_data = resampled_data.set_index('timestamp', append=True)
print(resampled_data)
ema_list = [3, 10, 20, 30]
window_list = [3, 10, 20, 30]
results = screener(resampled_data,
ema_list,
window_list,
plot=True,
results=True)
print(results)
def notify_trade(self,trade):
if not trade.isclosed:
return
dict_trade = {'price':trade.price, 'pnl' : trade.pnl}
trades = pd.DataFrame(dict_trade,index =[0])
trades.to_csv('output/supertrend_1.csv', mode='a', header=False)
if __name__ == '__main__':
# self.data.close[0] < self.data.open[0] or Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
# Get data from database
output = {}
allMinutePriceData = model.getMinutePriceData(start = datetime(2020,8,10) ,end = datetime(2020,12,25,0,0),
stockSymbol = 'BANKNIFTY DEC FUT')
DFList = [group[1] for group in allMinutePriceData.groupby(allMinutePriceData['datetime'].dt.date)]
for df in DFList:
df = df.set_index('datetime')
print(df.index.date[0])
cerebro = bt.Cerebro()
minutePriceData = bt.feeds.PandasData(
dataname=df)
# Add a strategy
cerebro.addstrategy(SuperTrendStat)
# Add the Data Feeds to Cerebro
cerebro.adddata(minutePriceData)
# Set our desired cash start
cerebro.broker.setcash(745000.0)
cerebro.addsizer(bt.sizers.AllInSizerInt)
selltrigger = True
self.log(''' target reached ''')
if diff < 0 and diffpercent < -self.stoploss:
self.log(f''' stoploss triggered {diff} ''')
selltrigger = True
if selltrigger == True:
self.sell()
self.bought = False
self.log('sold @ Close, %.2f' % self.data.close[0])
if __name__ == '__main__':
# Get data from database
allMinutePriceData = model.getMinutePriceData(start=datetime(2020, 11, 15),
end=datetime(2020, 12, 4))
finalOutput = {}
allMinutePriceData['symbol'].unique().tolist()
symbols = ['WIPRO', 'TCS']
for symbol in symbols:
print(symbol)
minutePriceData = allMinutePriceData[allMinutePriceData['symbol'] ==
symbol]
minutePriceData = minutePriceData.set_index('datetime')
output = {}
df = minutePriceData.resample('5T',
origin='start',
closed='left',
label='left').agg({
'open': 'first',
'high': 'max',
def start(self):
self.smas = {data: bt.indicators.SMA(data, period=self.p.period)
for data in self.datas}
def stop(self):
self.rets['over'] = list()
self.rets['under'] = list()
for data, sma in self.smas.items():
node = data._name, data.close[0], sma[0]
if data.close[0] > sma[0]:
self.rets['over'].append(node)
else:
self.rets['under'].append(node)
if __name__ == '__main__':
minutePriceData = model.getMinutePriceData(dayInterval = 5)
UniqueSymbols = minutePriceData.symbol.unique().tolist()
grouped = minutePriceData.groupby(minutePriceData.symbol)
cerebro = bt.Cerebro()
for symbol in UniqueSymbols :
minutePriceData = grouped.get_group(symbol)
minutePriceData = minutePriceData.set_index('datetime')
minutePriceData = bt.feeds.PandasData(
dataname=minutePriceData)
cerebro.adddata(minutePriceData,name=symbol)
cerebro.addanalyzer(Screener_SMA, period=20)
cerebro.run(runonce=False, stdstats=False, writer=True)
self.bought = False
self.sell()
self.already_traded = True
self.sl_hit += 1
self.log(''' xxxxxxxxxxxx DANGER xxxxx ''')
self.log(f''' sell @ stoploss Close {self.data.close[0]},{self.bought_price}:{self.data.close[0] - self.bought_price}''')
if __name__ == '__main__':
# self.data.close[0] < self.data.open[0] or Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
# Get data from database
output = {}
allMinutePriceData = model.getMinutePriceData(start = datetime(2020,12,1) ,end = datetime(2020,12,25,0,0))
finalOutput = {}
symbols = allMinutePriceData['symbol'].unique().tolist()
for symbol in symbols:
minutePriceData = allMinutePriceData[allMinutePriceData['symbol'] == symbol]
minutePriceData = minutePriceData.set_index('datetime')
if(minutePriceData['close'].mean() < 100 ):
break
output = {}
DFList = [group[1] for group in minutePriceData.groupby(minutePriceData.index.date)]
for df in DFList:
cerebro = bt.Cerebro()
minutePriceData = bt.feeds.PandasData(
dataname=df)
# Add a strategy
cerebro.addstrategy(ORB)
self.log(''' target reached ''')
if diff < 0 and diffpercent < -self.stoploss:
self.log(f''' stoploss triggered {diff} ''')
selltrigger = True
if selltrigger == True:
self.sell()
self.bought = False
self.log('sold @ Close, %.2f' % self.data.close[0])
if __name__ == '__main__':
# Get data from database
allMinutePriceData = model.getMinutePriceData(start=datetime(2020, 11, 20),
end=datetime(2020, 12, 4),
stockSymbol='TATAMOTORS')
finalOutput = {}
symbols = allMinutePriceData['symbol'].unique().tolist()
for symbol in symbols:
#print(symbol)
minutePriceData = allMinutePriceData[allMinutePriceData['symbol'] ==
symbol]
minutePriceData = minutePriceData.set_index('datetime')
output = {}
DFList = [
group[1]
for group in minutePriceData.groupby(minutePriceData.index.date)
]
for df in DFList:
cerebro = bt.Cerebro()
