import pandas as pd import ccxt import matplotlib.pyplot as plt import matplotlib.dates as mdates import json from datetime import datetime, timedelta import pytz # Prompt for the symbol and time frame symbols = input('Please input Symbol: ') timeframe = input("Please input time frame: ") # Initialize Binance Futures API binance = ccxt.binance({ 'options': {'defaultType': 'future'}, # Specify futures }) # Function to fetch historical data and calculate Z-Score def fetch_and_calculate_zscore(symbol, timeframe, since, limit=200, rolling_window=30): data = binance.fetch_ohlcv(symbol, timeframe=timeframe, since=since, limit=limit) df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']) # Convert timestamp to UTC datetime format df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True) # Calculate rolling mean, std, and Z-Score df['mean'] = df['close'].rolling(window=rolling_window).mean() df['std'] = df['close'].rolling(window=rolling_window).std() df['z_score'] = (df['close'] - df['mean']) / df['std'] # Initialize signal columns df['buy_signal'] = 0 df['sell_signal'] = 0 # Variables to track thresholds in_sell_signal = False in_buy_signal = False signal_triggered = False # Track if any signal was triggered # Iterate through the dataframe to track signals for i in range(1, len(df)): current_z = df.loc[i, 'z_score'] previous_z = df.loc[i - 1, 'z_score'] # Handle Z-score crossing extreme thresholds for sell signal if not in_sell_signal: # Z-score crosses above 1.85 (potential sell signal) if current_z > 1.85 and previous_z <= 1.85: print(f"Sell signal candidate at index {i}, Z-score = {current_z}") in_sell_signal = True # Handle Z-score crossing extreme thresholds for buy signal if not in_buy_signal: # Z-score crosses below -1.85 (potential buy signal) if current_z < -1.85 and previous_z >= -1.85: print(f"Buy signal candidate at index {i}, Z-score = {current_z}") in_buy_signal = True # Keep the signal active if the Z-score remains within the range if in_sell_signal: # Sell signal is triggered between 1.85 and 1 if 1 <= current_z <= 1.85: df.loc[i, 'sell_signal'] = 1 # Sell signal active print(f"Sell signal active at index {i}, Z-score = {current_z}") signal_triggered = True # Exit sell signal if Z-score falls below 1 elif current_z < 1: in_sell_signal = False print(f"Sell signal exited at index {i}, Z-score = {current_z}") if in_buy_signal: # Buy signal is triggered between -1.85 and -1 if -1.85 <= current_z <= -1: df.loc[i, 'buy_signal'] = 1 # Buy signal active print(f"Buy signal active at index {i}, Z-score = {current_z}") signal_triggered = True # Exit buy signal if Z-score rises above -1 elif current_z > -1: in_buy_signal = False print(f"Buy signal exited at index {i}, Z-score = {current_z}") return df # Convert time to local timezone (Philippine Time) utc_time = datetime.utcnow() philippine_tz = pytz.timezone('Asia/Manila') philippine_time = pytz.utc.localize(utc_time).astimezone(philippine_tz) # Format the time in your preferred format formatted_ph_time = philippine_time.strftime("%Y-%m-%d %H:%M:%S") # Function to update signals in JSON with Z-Score (Appending to file) def update_signal_json(symbol, df, json_data): # Extract latest data point latest_data = df.iloc[-1] # Check if the latest Z-score has a signal signal_status = "True" if latest_data['buy_signal'] == 1 or latest_data['sell_signal'] == 1 else "False" # Prepare new entry with real-time Z-Score signal_entry = { "symbol": symbol, "time_frame": timeframe, "date_and_time": latest_data['timestamp'].strftime("%Y-%m-%d %H:%M:%S"), "realtime_ph_time": formatted_ph_time, # Add the local Philippine time (UTC+8) "current_price": latest_data['close'], "zscore": latest_data['z_score'], "detection": signal_status # Add signal status } # Append new data to the existing list in json_data json_data.append(signal_entry) return json_data # Function to plot data def plot_data(btcdom_df, pair_df, btc_df, ax): ax.clear() # Clear previous plots # Plot Z-Scores for all pairs ax.plot(btcdom_df['timestamp'], btcdom_df['z_score'], label="BTCDOM/USDT Z-Score", color='blue', linestyle='-') ax.plot(pair_df['timestamp'], pair_df['z_score'], label=f"{symbols}/USDT Z-Score", color='orange', linestyle='-') ax.plot(btc_df['timestamp'], btc_df['z_score'], label="BTC/USDT Z-Score", color='gray', linestyle='-') # Add thresholds ax.axhline(y=2, color='red', linestyle='--', label='Overbought Threshold') ax.axhline(y=-2, color='green', linestyle='--', label='Oversold Threshold') # Plot Buy and Sell signals for BTCDOM/USDT ax.scatter(btcdom_df[btcdom_df['buy_signal'] == 1]['timestamp'], btcdom_df[btcdom_df['buy_signal'] == 1]['z_score'], marker='^', color='green', label='BTCDOM Buy Signal') ax.scatter(btcdom_df[btcdom_df['sell_signal'] == 1]['timestamp'], btcdom_df[btcdom_df['sell_signal'] == 1]['z_score'], marker='v', color='red', label='BTCDOM Sell Signal') # Plot signals for the other pair ax.scatter(pair_df[pair_df['buy_signal'] == 1]['timestamp'], pair_df[pair_df['buy_signal'] == 1]['z_score'], marker='^', color='green', alpha=0.5, label=f"{symbols} Buy Signal") ax.scatter(pair_df[pair_df['sell_signal'] == 1]['timestamp'], pair_df[pair_df['sell_signal'] == 1]['z_score'], marker='v', color='red', alpha=0.5, label=f"{symbols} Sell Signal") # Format plot ax.set_title(f"Z-Scores Signals {timeframe} for {symbols}/USDT Futures", fontsize=16) ax.set_xlabel("Time (UTC)", fontsize=12) ax.set_ylabel("Z-Score", fontsize=12) ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d %H:%M")) ax.legend(loc="upper left") ax.grid(True) plt.xticks(rotation=45) plt.draw() # Redraw the plot plt.pause(0.1) # Pause to allow plot to update # Function to run historical data processing def run_historical(): json_data = [] try: with open('signals.json', 'r') as file: json_data = json.load(file) except FileNotFoundError: pass fig, ax = plt.subplots(figsize=(14, 7)) # Set start and end dates for the loop start_date = datetime(2023, 1, 1) end_date = datetime(2024, 1, 1) # Loop through each month in the date range (or week, depending on your choice) current_date = start_date while current_date < end_date: # Set 'since' to the start of each month or week (whichever you prefer) since = binance.parse8601(current_date.strftime('%Y-%m-%dT%H:%M:%SZ')) btcdom_symbol = 'BTCDOM/USDT' pair_symbol = f'{symbols}/USDT' btc_symbol = 'BTC/USDT' # Fetch and process data btcdom_df = fetch_and_calculate_zscore(btcdom_symbol, timeframe, since) pair_df = fetch_and_calculate_zscore(pair_symbol, timeframe, since) btc_df = fetch_and_calculate_zscore(btc_symbol, timeframe, since) # Update signals and append to JSON json_data = update_signal_json(pair_symbol, pair_df, json_data) json_data = update_signal_json(btc_symbol, btc_df, json_data) json_data = update_signal_json(btcdom_symbol, btcdom_df, json_data) # Save updated signals to JSON with open('signals.json', 'w') as file: json.dump(json_data, file, indent=4) # Plot the data plot_data(btcdom_df, pair_df, btc_df, ax) # Display the plot after each loop plt.show() # Show the plot for the current iteration # Move to the next chunk (next month/week) current_date += timedelta(weeks=4) # Run the historical data processing run_historical()