Support Board

def __init__(self) -> None:

self.bcols = ['Time', 'Open', 'High', 'Low', 'Close', 'Volume', 'Trades', 'BidVolume', 'AskVolume']

def get_scid_df(self, filename, limitsize=sys.maxsize):

f = Path(filename)
assert f.exists(), "file not found"
stat = f.stat()
offset = 56 if stat.st_size < limitsize else stat.st_size - (
(limitsize // 40) * 40)
rectype = np.dtype([
(self.bcols[0], '<u8'), (self.bcols[1], '<f4'), (self.bcols[2], '<f4'),
(self.bcols[3], '<f4'), (self.bcols[4], '<f4'), (self.bcols[6], '<i4'),
(self.bcols[5], '<i4'), (self.bcols[7], '<i4'), (self.bcols[8], '<i4')
])
df = pd.DataFrame(data=np.memmap(f, dtype=rectype, offset=offset, mode="r"),copy=False)
df.dropna(inplace=True)
df["Time"] = df["Time"] - 2209161600000000
df.drop(df[(df.Time < 1) | (df.Time > 1705466561000000)].index, inplace=True)
df.set_index("Time", inplace=True)
df.index = pd.to_datetime(df.index, unit='us')
# df.index = df.index.tz_localize(tz="est")

return df

def resampling(self, df):
df_5min = (
df.resample("5Min").agg(
{
"Open": "first",
"High": "max",
"Low": "min",
"Close": "last",
"Volume": "sum",
"Trades": "sum",
"BidVolume": "sum",
"AskVolume": "sum",
}

).ffill()

)

df = df_5min[["Open", "High", "Low", "Close"]]
df.index.rename('Open time', inplace=True)

eastern = pytz.timezone('US/Eastern')
df.index = df.index.tz_localize(pytz.utc).tz_convert(eastern)
df.index = df.index.tz_localize(None)
df = df / 100 #Needed for CL prices

return df