mtf dips/tops finder btc

import numpy as np
from binance.client import Client
from binance.enums import *
import pandas as pd

# Load API key and secret from credentials.txt
with open('credentials.txt', 'r') as f:
    lines = f.readlines()
    api_key = lines[0].strip()
    api_secret = lines[1].strip()

# Connect to Binance Futures API
client = Client(api_key, api_secret, testnet=False)

# Retrieve price data for USDTM on all timeframes
symbols = ['BTCUSDT']
timeframes = ['1d', '4h', '1h', '15m', '5m']
prices = {}
for symbol in symbols:
    for timeframe in timeframes:
        klines = client.futures_klines(symbol=symbol, interval=timeframe, limit=1000)
        df = pd.DataFrame(klines, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'trades', 'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'])
        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
        df.set_index('timestamp', inplace=True)
        prices[(symbol, timeframe)] = df

# Perform polynomial fit analysis for all timeframes
for symbol in symbols:
    for timeframe in timeframes:
        df = prices[(symbol, timeframe)]
        x = df['close'].astype(float).values
        y = np.arange(len(x)).astype(float)
        best_fit_line1 = np.poly1d(np.polyfit(y, x, 1))(y)
        best_fit_line2 = np.poly1d(np.polyfit(y, x, 1))(y) * 1.01
        best_fit_line3 = np.poly1d(np.polyfit(y, x, 1))(y) * 0.99
        # Determine if the close price is below the lowest line
        if df['close'].astype(float)[-1] < min(best_fit_line1[-1], best_fit_line2[-1], best_fit_line3[-1]):
            print(f"{symbol} {timeframe}: DIP")
        else:
            print(f"{symbol} {timeframe}: TOP")