import requests
import hashlib
import time
# API認(rèn)證（示例代碼）
def auth_ths(username, password):
    timestamp = str(int(time.time()))
    sign = hashlib.md5(f"{password}{timestamp}".encode()).hexdigest()
    headers = {"User-Agent": "THS-SDK-Python"}
    response = requests.post(
        url="https://simtrade.ths.com/api/auth",
        json={"username": username, "timestamp": timestamp, "sign": sign},
        headers=headers
    )
    return response.json()["access_token"]
# 獲取實(shí)時(shí)行情（WebSocket示例）
import websocket
def on_message(ws, message):
    print(f"行情數(shù)據(jù): {message}")
ws = websocket.WebSocketApp(
    "wss://simquote.ths.com/ws",
    on_message=on_message
)
ws.run_forever()

from binance.spot import Spot
client = Spot(
    api_key="YOUR_API_KEY",
    api_secret="YOUR_SECRET_KEY",
    base_url="https://testnet.binance.vision"
)
# 市價(jià)單示例
order = client.new_order(
    symbol="BTCUSDT",
    side="BUY",
    type="MARKET",
    quantity=0.001
)
print(f"訂單ID: {order['orderId']}")

import dask
from dask.distributed import Client
from backtrader import analyzers
client = Client(n_workers=4)  # 啟動(dòng)4個(gè)并行進(jìn)程
@dask.delayed
def backtest(params):
    cerebro = bt.Cerebro()
    cerebro.addstrategy(MyStrategy, period=params['period'])
    cerebro.addanalyzer(analyzers.SharpeRatio, _name='sharpe')
    results = cerebro.run()
    return results[0].analyzers.sharpe.get_analysis()
# 參數(shù)空間
params_grid = {'period': range(10, 50, 5)}
results = []
for params in params_grid:
    results.append(backtest(params))
# 計(jì)算最優(yōu)參數(shù)
sharpe_ratios = dask.compute(*results)
optimal_params = params_grid[np.argmax(sharpe_ratios)]

import random
from deap import base, creator, tools
# 定義適應(yīng)度函數(shù)
def evaluate(individual):
    threshold, position = individual
    return (calculate_profit(threshold, position),)  # 最大化收益
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, 0.1, 0.5)  # 參數(shù)范圍
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=2)
toolbox.register("evaluate", evaluate)
toolbox.register("mate", tools.cxBlend, alpha=0.5)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.2)
toolbox.register("select", tools.selTournament, tournsize=3)
pop = toolbox.population(n=50)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("max", np.max)
result, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, 
                                 stats=stats, halloffame=hof, verbose=True)

import requests
from requests_oauthlib import OAuth2Session
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_SECRET"
redirect_uri = "https://localhost/callback"
oauth = OAuth2Session(client_id, redirect_uri=redirect_uri)
authorization_url, _ = oauth.authorization_url("https://api.htsc.com/oauth/authorize")
# 獲取授權(quán)碼后交換Token
token = oauth.fetch_token(
    "https://api.htsc.com/oauth/token",
    client_secret=client_secret,
    authorization_response=redirect_response
)
# 下單請(qǐng)求示例
order_params = {
    "symbol": "600519.SH",
    "price": 1900.0,
    "quantity": 100,
    "side": "BUY"
}
response = oauth.post("https://api.htsc.com/trade/order", json=order_params)

import hmac
import hashlib
import urllib.parse
def sign_request(secret, params):
    query_string = urllib.parse.urlencode(params)
    signature = hmac.new(secret.encode(), query_string.encode(), hashlib.sha256).hexdigest()
    return signature
params = {
    "symbol": "BTCUSDT",
    "side": "SELL",
    "type": "LIMIT",
    "quantity": 0.1,
    "timestamp": int(time.time() * 1000)
}
params["signature"] = sign_request("API_SECRET", params)

# 文件名: fast_order.pyx
cimport cython
@cython.boundscheck(False)
@cython.wraparound(False)
def process_order(double[:] prices, double[:] volumes, int window_size):
    cdef int n = prices.shape[0]
    cdef double total = 0.0
    cdef int i, j
    for i in range(n - window_size + 1):
        total = 0.0
        for j in range(window_size):
            total += prices[i + j] * volumes[i + j]
        # ...訂單處理邏輯
    return total

import redis
import json
r = redis.Redis(host='localhost', port=6379, db=0)
def cache_market_data(symbol, data):
    r.set(f"market:{symbol}", json.dumps(data), ex=60)  # 過期時(shí)間60秒
def get_cached_data(symbol):
    data = r.get(f"market:{symbol}")
    return json.loads(data) if data else None

def kelly_criterion(win_prob, win_loss_ratio, cost_rate=0.001):
    """
    :param win_prob: 勝率
    :param win_loss_ratio: 盈虧比（平均盈利/平均虧損）
    :param cost_rate: 交易成本占比
    """
    f = (win_prob * (win_loss_ratio + 1) - 1) / win_loss_ratio
    return max(0, f * (1 - cost_rate))  # 考慮交易成本
# 示例：勝率55%，盈虧比1.5，成本0.1%
position = kelly_criterion(0.55, 1.5, 0.001)
print(f"建議倉位: {position*100:.1f}%")

import pandas as pd
def calculate_atr(df, period=20):
    high_low = df['high'] - df['low']
    high_close = np.abs(df['high'] - df['close'].shift())
    low_close = np.abs(df['low'] - df['close'].shift())
    tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
    return tr.rolling(period).mean()
# 當(dāng)ATR超過歷史均值1.5倍時(shí)減倉
current_atr = calculate_atr(df).iloc[-1]
historical_mean = calculate_atr(df).mean()
if current_atr > 1.5 * historical_mean:
    adjust_position(current_position * 0.7)

class TrailingStop:
    def __init__(self, atr_period=14, multiplier=3):
        self.highest_price = -np.inf
        self.multiplier = multiplier
        self.atr = calculate_atr(df, atr_period)
    def update(self, current_price):
        self.highest_price = max(self.highest_price, current_price)
        stop_loss_price = self.highest_price - self.atr.iloc[-1] * self.multiplier
        return current_price < stop_loss_price
# 使用示例
trailing_stop = TrailingStop()
for price in live_prices:
    if trailing_stop.update(price):
        print("觸發(fā)止損！")
        exit_position()

import numpy as np
from sklearn.decomposition import PCA
# 計(jì)算策略收益協(xié)方差
returns = np.array([strategy1_returns, strategy2_returns, strategy3_returns])
cov_matrix = np.cov(returns)
# PCA降維分析
pca = PCA(n_components=2)
principal_components = pca.fit_transform(returns.T)
print("解釋方差比:", pca.explained_variance_ratio_)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
# 構(gòu)建LSTM模型
model = Sequential([
    LSTM(50, input_shape=(30, 1)),  # 30天歷史數(shù)據(jù)
    Dense(1, activation='sigmoid')  # 預(yù)測(cè)波動(dòng)率是否超過閾值
])
model.compile(optimizer='adam', loss='binary_crossentropy')
# 訓(xùn)練數(shù)據(jù)預(yù)處理
lookback = 30
X, y = [], []
for i in range(lookback, len(volatility)):
    X.append(volatility[i-lookback:i])
    y.append(1 if volatility[i] > threshold else 0)
X = np.array(X).reshape(-1, lookback, 1)
y = np.array(y)
model.fit(X, y, epochs=10)

pragma solidity ^0.8.0;
contract TradeAudit {
    struct Trade {
        address trader;
        string symbol;
        uint256 amount;
        uint256 timestamp;
    }
    Trade[] public trades;
    event TradeLogged(address indexed trader, string symbol, uint256 amount);
    function logTrade(string memory _symbol, uint256 _amount) public {
        trades.push(Trade(msg.sender, _symbol, _amount, block.timestamp));
        emit TradeLogged(msg.sender, _symbol, _amount);
    }
}