Hi everyone,

hopefully someone can enlighten me why the difference.

I'm retrieving data from Polygon and using python, calculating SMA20, SMA50 and EMA8 on the fly and store them along with the ohlc data in a sqlite3 table.

the default python calculations

sma20 = barsets['close'].rolling(window=20).mean()

VS

self.sma_20 = bt.indicators.SimpleMovingAverage(
self.datas[0], period=20)

I am using my Schwab account charts to compare my results and the default python values have always been exactly the same and because these are my benchmarks.

I don't think this is a Backtrader thing but it does force me to use pre calculated values
any ideas?

@rajanprabu Thank you, Thank you, thank you!

@ab_trader
thanks... wish extending lines was so straight forward...

i am so new to coding withthe volume of information i'm trying digest i skim over way too many details that make things work...
and end up here....

any assistance will be greatly appreciated

data = pd.read_sql("""
            select datetime, open, high, low, close, volume, sma_20, sma_50, ema_8, mom, lin_ang
            from stock_price_15
            where symbol = :symbol
            and sma_50 is not 0
            and datetime > '2021-01-26'
            and strftime('%H:%M:%S', datetime) >= '09:30:00' 
            and strftime('%H:%M:%S', datetime) < '16:00:00'
            order by datetime asc
        """, conn, params={"symbol": stock['symbol']}, index_col='datetime', parse_dates=['datetime'])

        data = bt.feeds.PandasDirectData(dataname=data)

        cerebro.adddata(data)

class OpeningRangeBreakout(bt.Strategy):

    params = (
        dict(num_opening_bars=15)
    )
 
    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        if dt is None:
            dt = self.datas[0].datetime.datetime(0)
        print('%s, %s' % (dt, txt))   

    def __init__(self):
        
        self.dataclose = self.datas[0].close

        # To keep track of
        self.opening_range_low = 0
        self.opening_range_high = 0
        self.opening_range = 0
        self.bought_today = False
        self.order = None
        self.buyprice = None
        self.buycomm = None

        ## ADD INDICATORS ###########
        self.sma_20 = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=20)
        self.sma_50 = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=50)
        # Indicators for the plotting show
        self.ema_8 = bt.indicators.ExponentialMovingAverage(self.datas[0], period=8)

    def next(self):
      
        current_bar_datetime = self.data.num2date(self.data.datetime[0])        
        previous_bar_datetime = self.data.num2date(self.data.datetime[-1])       
        two_bars_back = self.data.num2date(self.data.datetime[-2])
        three_bars_back = self.data.num2date(self.data.datetime[-3])
       
        self.log(self.data.close[0])

        #self.log(self.datavolume[0])


        #print(f'dataclose',(self.dataclose[0]) )
        #print(f'ema_8', self.ema_8[0])
        #print(f'sma_20', self.sma_20[0])
        #print(f'sma_50', self.sma_50[0])


        ############## THE FIRST SESSION OF THE NEW DAY  ROUTE 1####################
        if current_bar_datetime.date() != previous_bar_datetime.date():
            self.opening_range_high = self.data.high[0]
            opening_high = self.data.high
            #print(f'opening high', opening_high[0])
            self.opening_range_low = self.data.low[0]
            #print(f'opening range low',self.data.low[0])

            #### OPENING RANGE ###
            opening_range_start_time = time(9, 30, 0)
            dt = datetime.combine(datetime.today(), opening_range_start_time) + timedelta(minutes=self.p.num_opening_bars)
            opening_range_end_time = dt.time()
            ###################

            print(f'opening close', self.dataclose[0])

hell all,
no matter how many examples i try, i can't seem to get my columns to use.
i must have an error in what exactly i am calling to use on the bt.indicators because they don't match with the values in my dataframe...
and i think processing would be a lot faster if i only needed to calculate from my already known values..
this is for monitoring right now....

sorry, again, i tried pasting but it came out like below..

here is my code..

data = pd.read_sql("""
select datetime, open, high, low, close, volume, sma_20, sma_50, ema_8, mom, lin_ang
from stock_price_15
where symbol = :symbol
and sma_50 is not 0
and datetime > '2021-01-26'
and strftime('%H:%M:%S', datetime) >= '09:30:00'
and strftime('%H:%M:%S', datetime) < '16:00:00'
order by datetime asc
""", conn, params={"symbol": stock['symbol']}, index_col='datetime', parse_dates=['datetime'])

data = bt.feeds.PandasDirectData(dataname=data)
cerebro.adddata(data)

#CREATES THIS DF
== Analysing A ==
open high low close volume sma_20 sma_50 ema_8 mom
datetime
2021-01-26 09:30:00 125.50 125.95 124.550 125.040 92435 125.072505 125.919682 125.203493 0
2021-01-26 09:45:00 125.09 125.16 124.430 125.005 88188 125.070255 125.887382 125.159384 0
2021-01-26 10:00:00 125.04 125.36 124.740 124.845 39486 125.055005 125.850682 125.089521 0
2021-01-26 10:15:00 124.82 125.29 124.630 125.210 29410 125.072005 125.819482 125.116294 0
2021-01-26 10:30:00 125.24 125.66 125.170 125.550 43447 125.119005 125.791882 125.212673 0
... ... ... ... ... ... ... ... ... ...
2021-01-28 10:45:00 121.18 121.68 121.180 121.400 40882 120.019275 121.871370 120.669654 0
2021-01-28 11:00:00 121.43 121.89 121.340 121.870 43707 120.081025 121.810170 120.936397 0
2021-01-28 11:15:00 121.87 122.04 121.390 121.640 53618 120.141025 121.746970 121.092753 0
2021-01-28 11:30:00 121.65 122.25 121.550 122.195 37105 120.213775 121.691670 121.337697 0
2021-01-28 11:45:00 122.25 122.67 122.115 122.500 54387 120.31

then.............

create a Strategy

class OpeningRangeBreakout(bt.Strategy):
#class TestStrategy(bt.Strategy):
params = (
dict(num_opening_bars=15)
)

def log(self, txt, dt=None):
    ''' Logging function for this strategy'''
    if dt is None:
        dt = self.datas[0].datetime.datetime(0)
    print('%s, %s' % (dt, txt))

########## DEFINING THE TEMPLATE AND VARIABLES TO USE

def __init__(self):
  
    self.dataclose = self.datas[0].close

    self.opening_range_low = 0
    self.opening_range_high = 0
    self.opening_range = 0
    self.bought_today = False
    self.order = None
    self.buyprice = None
    self.buycomm = None

    ## ADD INDICATORS ###########
    self.sma_20 = bt.indicators.SimpleMovingAverage(
        self.datas[0], period=20)
    self.sma_50 = bt.indicators.SimpleMovingAverage(
        self.datas[0], period=50)
    # Indicators for the plotting show
    self.ema_8 = bt.indicators.ExponentialMovingAverage(self.datas[0], period=8)

def next(self):

    ######### THE CURRENT SESSION #########################################
    current_bar_ = self.data.num2date(self.data.datetime[0])       
    previous_bar = self.data.num2date(self.data.datetime[-1])        
    two__bars_back = self.data.num2date(self.data.datetime[-2])
    three_bars_back = self.data.num2date(self.data.datetime[-3])

@toinou222 said in extending bt.feeds.PandasData:

class custom_data_loader(btfeeds.PandasData):
lines = ("signal","prediction",)
params = (("signal", -1),("prediction", -1))
datafields = btfeeds.PandasData.datafields + (["prediction", "signal",])

thanks, i'll try that out

I surrender!
I am reading the data from pd.read_sql and all is well and i can run a "generic" strategy
BUT
no matter how many different ways i've tried, I can't figure out how to include the "int1", "int2", etc. columns so that i can call them like ohlc values into the strategy.

what and where do i put it?
please?

@armaan-syed hi armaan, by any chance did you ever figure this out?
I'm at the same obstacle and would appreciate some advice