Код не работает на M1 Pro Mac, но работает на Intel. Как это решить? ⇐ Python
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Anonymous
Код не работает на M1 Pro Mac, но работает на Intel. Как это решить?
from binance.enums import * # from binance.client import Client from binance.streams import ThreadedWebsocketManager import pandas as pd import os import nest_asyncio nest_asyncio.apply() # Define the structure of your DataFrame columns = ['timestamp', 'best_bid_price', 'best_bid_qty', 'best_ask_price', 'best_ask_qty'] logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Buffer to store incoming data buffer = [] BUFFER_SIZE = 3 # 10000 def process_message(msg): print(30 * "-") # logging.info("Message received") # logging.info(f"Message : \n{msg}") global buffer timestamp = pd.to_datetime(msg['E'], unit='ms') # print("timestamp : ", timestamp) # Assuming 'b' and 'a' are lists containing best bid and ask data, respectively # Each entry in 'b' or 'a' is assumed to be [price, quantity] best_bid = msg['b'][0] if msg['b'] else [None, None] # Take the first or set None if empty best_ask = msg['a'][0] if msg['a'] else [None, None] # Take the first or set None if empty buffer.append([timestamp, best_bid[0], best_bid[1], best_ask[0], best_ask[1]]) if len(buffer) >= BUFFER_SIZE: update_dataframe() def update_dataframe(): global buffer, order_book_df, columns print(30 * "-") # print("buffer : \n", buffer) logging.info(f"Updating DataFrame with {len(buffer)} entries") temp_df = pd.DataFrame(buffer, columns=columns) temp_df.set_index('timestamp', inplace=True) # print("temp_df info : \n", temp_df.info()) print("temp_df : \n", temp_df) temp_df['best_bid_price'] = pd.to_numeric(temp_df['best_bid_price']) temp_df['best_bid_qty'] = pd.to_numeric(temp_df['best_bid_qty']) temp_df['best_ask_price'] = pd.to_numeric(temp_df['best_ask_price']) temp_df['best_ask_qty'] = pd.to_numeric(temp_df['best_ask_qty']) # temp_df['best_bid_price'] = fill_nulls(temp_df['best_bid_price']) # temp_df['best_bid_qty'] = fill_nulls(temp_df['best_bid_qty']) # temp_df['best_ask_price'] = fill_nulls(temp_df['best_ask_price']) # temp_df['best_ask_qty'] = fill_nulls(temp_df['best_ask_qty']) temp_df = fill_nulls(temp_df) # print("temp_df num : \n", temp_df) print("temp_df (Buffer) : \n", temp_df) order_book_df = pd.concat([order_book_df, temp_df]) # , ignore_index=True) buffer.clear() # Clear the buffer after updating # Optionally, trim DataFrame order_book_df = order_book_df.tail(1000) # print(30 * "-") # print("order_book_df info : \n", order_book_df.info()) # print("order_book_df : \n", order_book_df) def fill_nulls(data): global order_book_df, columns for col in columns[1:]: for i in range(len(data)): if math.isnan(data[col].iloc): if i == 0: try : data[col].iloc = order_book_df[col].iloc[-1] except Exception as e: print(e) data[col].iloc = data[col].mean() else : data[col].iloc = data[col].iloc[i-1] # prev_data = data.shift().fillna(data.mean()) # next_data = data.shift(-1).fillna(data.mean()) # data = data.fillna(prev_data + next_data / 2) return data def analyze_data(): global order_book_df, twm while True: sleep(5) # Analysis frequency # logging.info("Starting analysis") try : if not order_book_df.empty: # logging.info("Data not empty !") # print(order_book_df.tail()) trades = check_severe_exhaustion_and_trend_reversal(order_book_df.copy()) # , 100, 6) # Copy to avoid potential issues with threading trades_df = pd.DataFrame(trades) print("trades : \n", trades_df) except Exception as e : print(e) pass # Initialize your DataFrame and WebSocket order_book_df = pd.DataFrame(columns=columns) order_book_df.set_index('timestamp', inplace=True) api_key = os.getenv('API_KEY') api_secret = os.getenv('API_SECRET') twm = ThreadedWebsocketManager(api_key = api_key, api_secret = api_secret, testnet = True) twm.start() twm.start_depth_socket(callback=process_message, symbol='1000PEPEUSDT') # Start analysis thread analysis_thread = threading.Thread(target=analyze_data) analysis_thread.start() # Keep the main thread running twm.join() I'm trying to run a trading strategy using binance websocket API, this strategy uses orderbook exhaustion, I haven't included it, only the most relevant part of the code.
When i try running my code it doesn't work, it used to before including making trades.
The check_severe_exhaustion_and_trend_reversal() function essentially returns a dataframe of trades in this format:
{ 'Entry Time': '2024-03-04 09:00:00', 'Exit Time': '2024-03-04 12:00:00', 'Entry Price': 100, 'Exit Price': 105, 'Percentage Profit': 5.0 } I've tried using a threading.Lock() inside process_message() and analyze_data(). In addition I've debugged the code in VSCode but since it can only debug the main thread, I can't find where something goes wrong.
Источник: https://stackoverflow.com/questions/781 ... solve-this
from binance.enums import * # from binance.client import Client from binance.streams import ThreadedWebsocketManager import pandas as pd import os import nest_asyncio nest_asyncio.apply() # Define the structure of your DataFrame columns = ['timestamp', 'best_bid_price', 'best_bid_qty', 'best_ask_price', 'best_ask_qty'] logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Buffer to store incoming data buffer = [] BUFFER_SIZE = 3 # 10000 def process_message(msg): print(30 * "-") # logging.info("Message received") # logging.info(f"Message : \n{msg}") global buffer timestamp = pd.to_datetime(msg['E'], unit='ms') # print("timestamp : ", timestamp) # Assuming 'b' and 'a' are lists containing best bid and ask data, respectively # Each entry in 'b' or 'a' is assumed to be [price, quantity] best_bid = msg['b'][0] if msg['b'] else [None, None] # Take the first or set None if empty best_ask = msg['a'][0] if msg['a'] else [None, None] # Take the first or set None if empty buffer.append([timestamp, best_bid[0], best_bid[1], best_ask[0], best_ask[1]]) if len(buffer) >= BUFFER_SIZE: update_dataframe() def update_dataframe(): global buffer, order_book_df, columns print(30 * "-") # print("buffer : \n", buffer) logging.info(f"Updating DataFrame with {len(buffer)} entries") temp_df = pd.DataFrame(buffer, columns=columns) temp_df.set_index('timestamp', inplace=True) # print("temp_df info : \n", temp_df.info()) print("temp_df : \n", temp_df) temp_df['best_bid_price'] = pd.to_numeric(temp_df['best_bid_price']) temp_df['best_bid_qty'] = pd.to_numeric(temp_df['best_bid_qty']) temp_df['best_ask_price'] = pd.to_numeric(temp_df['best_ask_price']) temp_df['best_ask_qty'] = pd.to_numeric(temp_df['best_ask_qty']) # temp_df['best_bid_price'] = fill_nulls(temp_df['best_bid_price']) # temp_df['best_bid_qty'] = fill_nulls(temp_df['best_bid_qty']) # temp_df['best_ask_price'] = fill_nulls(temp_df['best_ask_price']) # temp_df['best_ask_qty'] = fill_nulls(temp_df['best_ask_qty']) temp_df = fill_nulls(temp_df) # print("temp_df num : \n", temp_df) print("temp_df (Buffer) : \n", temp_df) order_book_df = pd.concat([order_book_df, temp_df]) # , ignore_index=True) buffer.clear() # Clear the buffer after updating # Optionally, trim DataFrame order_book_df = order_book_df.tail(1000) # print(30 * "-") # print("order_book_df info : \n", order_book_df.info()) # print("order_book_df : \n", order_book_df) def fill_nulls(data): global order_book_df, columns for col in columns[1:]: for i in range(len(data)): if math.isnan(data[col].iloc): if i == 0: try : data[col].iloc = order_book_df[col].iloc[-1] except Exception as e: print(e) data[col].iloc = data[col].mean() else : data[col].iloc = data[col].iloc[i-1] # prev_data = data.shift().fillna(data.mean()) # next_data = data.shift(-1).fillna(data.mean()) # data = data.fillna(prev_data + next_data / 2) return data def analyze_data(): global order_book_df, twm while True: sleep(5) # Analysis frequency # logging.info("Starting analysis") try : if not order_book_df.empty: # logging.info("Data not empty !") # print(order_book_df.tail()) trades = check_severe_exhaustion_and_trend_reversal(order_book_df.copy()) # , 100, 6) # Copy to avoid potential issues with threading trades_df = pd.DataFrame(trades) print("trades : \n", trades_df) except Exception as e : print(e) pass # Initialize your DataFrame and WebSocket order_book_df = pd.DataFrame(columns=columns) order_book_df.set_index('timestamp', inplace=True) api_key = os.getenv('API_KEY') api_secret = os.getenv('API_SECRET') twm = ThreadedWebsocketManager(api_key = api_key, api_secret = api_secret, testnet = True) twm.start() twm.start_depth_socket(callback=process_message, symbol='1000PEPEUSDT') # Start analysis thread analysis_thread = threading.Thread(target=analyze_data) analysis_thread.start() # Keep the main thread running twm.join() I'm trying to run a trading strategy using binance websocket API, this strategy uses orderbook exhaustion, I haven't included it, only the most relevant part of the code.
When i try running my code it doesn't work, it used to before including making trades.
The check_severe_exhaustion_and_trend_reversal() function essentially returns a dataframe of trades in this format:
{ 'Entry Time': '2024-03-04 09:00:00', 'Exit Time': '2024-03-04 12:00:00', 'Entry Price': 100, 'Exit Price': 105, 'Percentage Profit': 5.0 } I've tried using a threading.Lock() inside process_message() and analyze_data(). In addition I've debugged the code in VSCode but since it can only debug the main thread, I can't find where something goes wrong.
Источник: https://stackoverflow.com/questions/781 ... solve-this
