import ccxt
import pandas as pd
import numpy as np
from scipy.stats import linregress
import os
import matplotlib.pyplot as plt
from scipy.signal import find_peaks


# Configurations
base_currencies = {'1': 'BTC', '2': 'ETH', '3': 'USDT'}
timeframes = {'1': '5m', '2': '15m', '3': '1h', '4': '4h'}

# User inputs for base currency and timeframe
base_choice = input("Choose the base currency (1: BTC, 2: ETH, 3: USDT): ")
timeframe_choice = input("Select the timeframe (1: 5m, 2: 15m, 3: 1h, 4: 4h): ")

base_currency = base_currencies[base_choice]
timeframe = timeframes[timeframe_choice]

# Define plot_directory globally
plot_directory = os.path.join(os.getcwd(), 'wedge_plots')
os.makedirs(plot_directory, exist_ok=True)  # Create the directory if it does not exist.

# Connect to Binance
exchange = ccxt.binance({
'apiKey': 'YOUR_API_KEY', # Replace with your API key
'secret': 'YOUR_SECRET_KEY', # Replace with your secret key
})

# Function to fetch data
def fetch_data(symbol, timeframe):
  bars = exchange.fetch_ohlcv(symbol, timeframe)
  df = pd.DataFrame(bars, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
  df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
  return df

# Define the is_wedge_converging function (replace any existing implementation)
def is_wedge_converging(data, high_slope, low_slope, threshold, atr_window):
  """
  This function checks if a wedge pattern is converging with a price squeeze near the tip.

  Args:
      data (pandas.DataFrame): The dataframe containing OHLC prices.
      high_slope (float): The slope of the linear regression line for highs.
      low_slope (float): The slope of the linear regression line for lows.
      threshold (float): Threshold for slope convergence (absolute value).
      atr_window (int): Window size for calculating Average True Range (ATR).

  Returns:
      bool: True if the wedge is converging with a price squeeze near the tip, False otherwise.
  """

  # Check slope convergence
  if abs(high_slope) > threshold or abs(low_slope) > threshold:
    return False

  # Calculate Average True Range (ATR)
  atr = data['High'].diff(1).abs().max(axis=1)  # True Range
  atr = atr.ewm(alpha=1/atr_window, min_periods=atr_window).mean()  # Exponential Moving Average

  # Check price proximity to wedge tip (last 10 candles)
  last_candles = data.iloc[-10:]
  wedge_tip = (high_slope * len(data) + high_intercept)[-1]  # Price at the tip based on trendline

  # Adjust for ascending vs. descending wedge based on slope signs
  if high_slope > 0:  # Ascending wedge
    price_condition = last_candles['High'] >= wedge_tip - (atr[-1] * 0.05)  # Within 5% of upper line with ATR buffer
  else:  # Descending wedge
    price_condition = last_candles['Low'] <= wedge_tip + (atr[-1] * 0.05)  # Within 5% of lower line with ATR buffer

  # Check if any of the last 10 candles meet the price condition
  return price_condition.any()

# Function to plot the wedge (replace any existing implementation)
def plot_wedge(df, high_peaks, low_peaks, upper_line_coeffs, lower_line_coeffs, symbol, timeframe):
  colors = [cl if cl > op else 'red' for cl, op in zip(df['close'], df['open'])]  # Color bars based on close-open