How to get the data as array of the sma1 and sma2

Ronan Dunham

HI BT community,
I need sma1 and sma2 and date and closing price of a stock. I tried all my best to extract this data from the code below but somehow could not get the final data in a dataframe. Can someone help with this please? All I need is a dataframe with columns: DateTime, Closing Price, SMA1(which is 10 SMA) and SMA2(which is 30SMA). Any help is much appreciated.

import backtrader as bt

class SmaCross(bt.SignalStrategy):
    def __init__(self):
        sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)
        crossover = bt.ind.CrossOver(sma1, sma2)
        self.signal_add(bt.SIGNAL_LONG, crossover)

cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)

data0 = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1),
                                  todate=datetime(2012, 12, 31))
cerebro.adddata(data0)

cerebro.run()
cerebro.plot()

Ronan Dunham

Below is the complete and correct code:

The code works I am looking to get a dataframe with data as below:

class SmaCross(bt.SignalStrategy):
    def __init__(self):
        sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)
        crossover = bt.ind.CrossOver(sma1, sma2)
        self.signal_add(bt.SIGNAL_LONG, crossover)

cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)
store = alpaca_backtrader_api.AlpacaStore(key_id=ALPACA_API_KEY,secret_key=ALPACA_SECRET_KEY,paper=ALPACA_PAPER)
DataFactory = store.getdata

data0 = DataFactory(
        dataname=symbol_to_check,
        timeframe=bt.TimeFrame.TFrame("Minutes"),
        fromdate=pd.Timestamp('2019-11-15'),compression =1,
#         todate=pd.Timestamp('2018-11-17'),
        historical=True)

cerebro.adddata(data0)

cerebro.run()
cerebro.plot()

stochastick

Maybe try something list this.. Initialize empty list during init, and add to it during next(). Finally, during stop(), combine into a df and export/use for your own purposes.

@Ronan-Dunham said in How to get the data as array of the sma1 and sma2:

class SmaCross(bt.SignalStrategy):
    def __init__(self):
        sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)
        crossover = bt.ind.CrossOver(sma1, sma2)
        self.signal_add(bt.SIGNAL_LONG, crossover)
        self.ma1 = []     # Initialize empty lists to hold indicator values
        self.ma2 = []     # Initialize empty lists to hold indicator values

    def next(self):
        # Create data frame columns here by appending data to a list
        self.ma1.append(sma1)   
        self.ma2.append(sma2)   

    def stop(self):
        # Combine and export pandas DataFrame here   

cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)
store = alpaca_backtrader_api.AlpacaStore(key_id=ALPACA_API_KEY,secret_key=ALPACA_SECRET_KEY,paper=ALPACA_PAPER)
DataFactory = store.getdata

data0 = DataFactory(
        dataname=symbol_to_check,
        timeframe=bt.TimeFrame.TFrame("Minutes"),
        fromdate=pd.Timestamp('2019-11-15'),compression =1,
#         todate=pd.Timestamp('2018-11-17'),
        historical=True)

cerebro.adddata(data0)

cerebro.run()
cerebro.plot()

Ronan Dunham

Hi @stochastick thank you so much for the reply. I am super new to object oriented programming. How do i combine in df after stop() and how to call this data in the main program outside the class? Also how about the stock closing price and date.
Sorry for the silly ask but as you can tell I am embarrassingly new to OOP.

stochastick

There is most certainly a more efficient way to accomplish what you seek, but nonetheless here is a working sample that accomplishes I believe what you are searching for. The data frame is simply saved to CSV here, but you could do whatever you want with it. Data is loaded from my own sources, but just substitute with yours.

import backtrader as bt
import pandas as pd


class SmaCross(bt.SignalStrategy):

    # Global Parameters
    globalParams = {'symbols': ['EUR_USD'],
                    'startDate': '01-01-2018',
                    'endDate': '01-05-2018'}

    def __init__(self):
        self.sma1 = bt.ind.SMA(period=10)
        self.sma2 = bt.ind.SMA(period=30)
        self.crossover = bt.ind.CrossOver(self.sma1, self.sma2)
        self.signal_add(bt.SIGNAL_LONG, self.crossover)

        # Initialize empty lists to hold values
        self.date_time = []
        self.close_px = []
        self.ma1 = []
        self.ma2 = []

    def log(self, txt, dt=None):
        """ Logging function """
        dt = dt or self.datas[0].datetime.datetime()
        print('%s, %s' % (dt.isoformat(), txt))

    def next(self):
        """Log data to console and save to lists"""
        
        self.log('%s '
                 '| O %.5f '
                 '| H %.5f '
                 '| L %.5f '
                 '| C %.5f '
                 '| SMA1 %.5f '
                 '| SMA2 %.5f '
                 %
              (self.datas[0]._name,
               self.datas[0].open[0],
               self.datas[0].high[0],
               self.datas[0].low[0],
               self.datas[0].close[0],
               self.sma1[0],
               self.sma2[0]))

        self.date_time.append(self.datas[0].datetime.datetime())
        self.close_px.append(self.datas[0].close[0])
        self.ma1.append(self.sma1[0])
        self.ma2.append(self.sma2[0])

    def stop(self):
        df = pd.DataFrame(index=self.date_time)
        df['Close'] = self.close_px
        df['SMA1'] = self.ma1
        df['SMA2'] = self.ma2
        df.to_csv('trade_history.csv', sep='\t')


cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)

symbols = SmaCross.globalParams['symbols']

for symbol in symbols:
    mktData = pd.read_json('../sample-data/FX/training_' + symbol + '.json')
    mktData['time'] = pd.to_datetime(mktData['time'])
    timeSlice = ((mktData['time'] > SmaCross.globalParams['startDate']) &
                 (mktData['time'] < SmaCross.globalParams['endDate']))
    mktData = mktData.loc[timeSlice]
    mktData = bt.feeds.PandasData(dataname=mktData, timeframe=bt.TimeFrame.Minutes, datetime='time', close='close')
    cerebro.resampledata(mktData, timeframe=bt.TimeFrame.Minutes, compression=5, name=symbol)

cerebro.run()

This creates a CSV file formatted as such.

Ronan Dunham

@stochastick Thank you so much. I got the data that I wanted:

```

Date Time	Close	SMA1	SMA2
0	2019-11-15 13:15:00	351.8501	351.34751	350.981837
1	2019-11-15 13:16:00	351.8500	351.44551	351.036837
2	2019-11-15 13:18:00	351.6800	351.51351	351.091170
3	2019-11-15 13:19:00	351.6000	351.57351	351.117837
4	2019-11-15 13:20:00	351.7000	351.62351	351.181170
...	...	...	...	...
15809	2019-12-28 00:50:00	430.7600	430.72199	430.634997
15810	2019-12-28 00:53:00	430.5000	430.70199	430.634997
15811	2019-12-28 00:56:00	430.3300	430.65499	430.635663
15812	2019-12-28 00:57:00	430.8000	430.66499	430.648997
15813	2019-12-28 00:59:00	430.7500	430.66999	430.654663
15814 rows × 4 columns

Now all i need is this data without a text file. Just as a dataframe to use it in Jupyter cell. I donot need to make any strategy at all (again all i need is data from this specific source, other source like api.get_barset was missing a lot of data for me and this seem to work.)

Is there a way that is efficient to get this done without having to read from a csv file and directly use df in the cell?

Thanks a lot again.

Ronan Dunham

Hi All,

Can you help me get the dataframe df from stop(self) to be used in a jupyter cell without having to write in a csv file? Any help is greatly appreciated.

Ronan Dunham

Got it it works with SmaCross.df =df.

Thank you