import json
import pandas as pd
import queue
import threading
from collections import deque
from scipy.optimize import minimize
from logger_config import logger
import numpy as np

# 假设我们有一个数据流，订单簿和成交数据
order_book_snapshots = deque(maxlen=1000)  # 存储过去10000个订单簿快照

# 数据积累的阈值
DATA_THRESHOLD = 20
messages = queue.Queue()  # 创建一个线程安全队列

stop_event = threading.Event()

# 初始参数
k_initial = 0.5
A_initial = 1.0

# 假设S0是初始的参考价格
S0 = -1
sigma = 0.2  # 假设一个波动率


def on_message_depth(_ws, message):
    global order_book_snapshots
    json_message = json.loads(message)
    bids = [[float(price), float(quantity)] for price, quantity in json_message['data']['b'][:10]]
    asks = [[float(price), float(quantity)] for price, quantity in json_message['data']['a'][:10]]
    timestamp = pd.to_datetime(json_message['data']['E'], unit='ms')
    order_book_snapshots.append({
        'bids': bids,
        'asks': asks,
        'timestamp': timestamp
    })

    if len(order_book_snapshots) >= DATA_THRESHOLD:
        process_depth_data(order_book_snapshots)


def phi(k, S, S0):
    return np.exp(k * (S - S0))


def target_function(params, S_values):
    A, k = params
    r = 0
    for S in S_values:
        term1 = np.log(phi(k, S, S0))
        r += (term1 - np.log(A)) ** 2
    return r


def process_depth_data(order_book_snapshots):
    global k_initial, A_initial, S0
    S_values = [((snapshot['bids'][0][0] + snapshot['asks'][0][0]) / 2) for snapshot in order_book_snapshots]  # 使用买价计算

    if S0 == 0:
        S0 = S_values[0]

    # 使用优化算法最小化目标函数，找到最优的A和k
    params = np.array([A_initial, k_initial])
    result = minimize(target_function, params, args=(S_values,))
    A_opt, k_opt = result.x
    logger.info("最优参数 A:" + str(A_opt) + " k:" + str(k_opt))

    # 更新初始参数
    A_initial = A_opt
    k_initial = k_opt