Unable to retrieve historical data

import requests
import pandas as pd
import datetime

# API credentials
api_key = "API_key"  # Replace with your actual API key
access_token = "access_token"  # Replace with your actual access token

base_url = "https://api.kite.trade"
authorization = f"token {api_key}:{access_token}"

# Function to fetch instrument token
def get_instrument_token(symbol):
    url = f"{base_url}/instruments"
    headers = {
        "X-Kite-Version": "3",
        "Authorization": authorization
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        instruments = response.text.splitlines()
        for line in instruments[1:]:  # Skip the header line
            columns = line.split(",")
            if len(columns) > 2 and columns[2] == symbol:  # Match tradingsymbol
                return int(columns[0])  # Return instrument token
        raise ValueError(f"Instrument {symbol} not found!")
    else:
        raise Exception(f"Failed to fetch instruments: {response.text}")

# Function to fetch historical data
def get_historical_data(symbol, from_date, to_date, interval="day"):
    instrument_token = get_instrument_token(symbol)
    url = f"{base_url}/historical/{instrument_token}/{interval}"
    headers = {
        "X-Kite-Version": "3",
        "Authorization": authorization
    }
    params = {
        "from": from_date,
        "to": to_date
    }

    response = requests.get(url, headers=headers, params=params)
    if response.status_code == 200:
        data = response.json()
        candles = data.get("data", {}).get("candles", [])
        if not candles:
            raise Exception("No historical data returned.")
        return pd.DataFrame(candles, columns=["date", "open", "high", "low", "close", "volume"])
    else:
        raise Exception(f"Failed to fetch historical data: {response.text}")

# Function to analyze trades
def analyze_trades(symbol, from_date, to_date):
    df = get_historical_data(symbol, from_date, to_date)
    df["SMA_50"] = df["close"].rolling(window=50).mean()
    df["SMA_200"] = df["close"].rolling(window=200).mean()

    df["Signal"] = 0
    df.loc[df["SMA_50"] > df["SMA_200"], "Signal"] = 1
    df.loc[df["SMA_50"] < df["SMA_200"], "Signal"] = -1

    df["Returns"] = df["close"].pct_change()
    df["Trade_Return"] = df["Signal"].shift(1) * df["Returns"]

    win_ratio = len(df[df["Trade_Return"] > 0]) / len(df[df["Signal"] != 0]) if len(df[df["Signal"] != 0]) > 0 else 0
    loss_ratio = len(df[df["Trade_Return"] < 0]) / len(df[df["Signal"] != 0]) if len(df[df["Signal"] != 0]) > 0 else 0

    print(f"Win Ratio: {win_ratio}")
    print(f"Loss Ratio: {loss_ratio}")

    return df

# Function to display or save analysis
def display_analysis(symbol, from_date, to_date):
    analysis_df = analyze_trades(symbol, from_date, to_date)
    print(analysis_df.tail())
    analysis_df.to_csv(f"{symbol}_trade_analysis_{from_date}_{to_date}.csv", index=False)

# Main usage
if __name__ == "__main__":
    symbol = "TATAMOTORS"  # Example: Tata Motors
    from_date = "2023-01-01"  # Start date
    to_date = "2023-12-19"    # End date

    try:
        display_analysis(symbol, from_date, to_date)
    except Exception as e:
        print(f"Error: {e}")