binance的gp demo

.gitignore

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+.idea

binance_gp_demo.py

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+import threading
+
+import websocket
+import json
+import pandas as pd
+import numpy as np
+from scipy.optimize import minimize
+import time
+import warnings
+import logging
+import colorlog
+
+# 忽略 FutureWarning
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+# 配置日志
+handler = colorlog.StreamHandler()
+handler.setFormatter(colorlog.ColoredFormatter(
+    "%(log_color)s%(asctime)s - %(name)s - %(levelname)s \n %(message)s",
+    datefmt=None,
+    reset=True,
+    log_colors={
+        'DEBUG': 'cyan',
+        'INFO': 'blue',
+        'WARNING': 'yellow',
+        'ERROR': 'red',
+        'CRITICAL': 'bold_red',
+    }
+))
+logger = logging.getLogger("binance_gp_demo")
+logger.setLevel(logging.INFO)
+logger.addHandler(handler)
+
+# 步骤二：订阅Binance的成交数据和订单簿数据
+# Binance WebSocket API URL
+SOCKET_TRADE = "wss://stream.binance.com:9443/ws/notusdt@trade"
+SOCKET_DEPTH = "wss://stream.binance.com:9443/ws/notusdt@depth20@100ms"
+
+# Initialize the DataFrame
+df_trades = pd.DataFrame(columns=['price', 'qty', 'timestamp'])
+df_order_book = pd.DataFrame(columns=['bid_price', 'bid_qty', 'ask_price', 'ask_qty'])
+
+
+def on_message_trade(_ws, message):
+    global df_trades
+    json_message = json.loads(message)
+    trade = {
+        'price': float(json_message['p']),
+        'qty': float(json_message['q']),
+        'timestamp': pd.to_datetime(json_message['T'], unit='ms')
+    }
+    trade_df = pd.DataFrame([trade])
+    if not trade_df.empty and not trade_df.isna().all().all():
+        df_trades = pd.concat([df_trades, trade_df], ignore_index=True)
+
+
+# Function to handle order book messages
+def on_message_depth(_ws, message):
+    global df_order_book
+    json_message = json.loads(message)
+    bids = json_message['bids'][:10]  # Top 10 bids
+    asks = json_message['asks'][:10]  # Top 10 asks
+    order_book = {
+        'bid_price': [float(bid[0]) for bid in bids],
+        'bid_qty': [float(bid[1]) for bid in bids],
+        'ask_price': [float(ask[0]) for ask in asks],
+        'ask_qty': [float(ask[1]) for ask in asks]
+    }
+    df_order_book = pd.DataFrame([order_book])
+
+
+def on_error(_ws, error):
+    logger.error(error)
+
+
+def on_open(_ws):
+    print("### opened ###")
+
+
+# Create a WebSocket app
+ws_trade = websocket.WebSocketApp(SOCKET_TRADE, on_message=on_message_trade, on_error=on_error)
+ws_depth = websocket.WebSocketApp(SOCKET_DEPTH, on_message=on_message_depth, on_error=on_error)
+
+# 定义要传递给 run_forever 的参数
+http_proxy_host = "127.0.0.1"
+http_proxy_port = 7890
+proxy_type = "http"
+# Run the WebSocket with proxy settings
+trade_thread = threading.Thread(target=ws_trade.run_forever, kwargs={
+    'http_proxy_host': http_proxy_host,
+    'http_proxy_port': http_proxy_port,
+    'proxy_type': proxy_type
+})
+depth_thread = threading.Thread(target=ws_depth.run_forever, kwargs={
+    'http_proxy_host': http_proxy_host,
+    'http_proxy_port': http_proxy_port,
+    'proxy_type': proxy_type
+})
+trade_thread.start()
+depth_thread.start()
+
+# 步骤三：价差随机过程建模
+# Define the states and transition matrix
+states = ['tight', 'normal', 'wide']
+transition_matrix = np.zeros((3, 3))
+
+
+# Function to update the transition matrix based on historical data
+def update_transition_matrix(df):
+    global transition_matrix
+    for i in range(len(df) - 1):
+        current_state = df['state'].iloc[i]
+        next_state = df['state'].iloc[i + 1]
+        transition_matrix[states.index(current_state), states.index(next_state)] += 1
+    transition_matrix = transition_matrix / transition_matrix.sum(axis=1, keepdims=True)
+
+
+# Function to classify the spread into states
+def classify_spread(spread):
+    if spread < 0.01:
+        return 'tight'
+    elif spread < 0.02:
+        return 'normal'
+    else:
+        return 'wide'
+
+
+# Function to calculate spread and classify it
+def calculate_and_classify_spread():
+    global df_trades
+    df_trades['spread'] = df_trades['price'].diff().abs()
+    df_trades['state'] = df_trades['spread'].apply(classify_spread)
+
+
+# Update the transition matrix periodically
+def update_transition_matrix_periodically():
+    calculate_and_classify_spread()
+    update_transition_matrix(df_trades)
+    logger.info("Transition Matrix:\n%s", transition_matrix)
+
+
+# Run the update function periodically, e.g., every minute
+transition_matrix_update_thread = threading.Timer(60.0, update_transition_matrix_periodically)
+transition_matrix_update_thread.start()