量化开发者的技术栈全解析：从C++交易系统到Python回测引擎

// 使用对象池避免高频new/delete
template<typename T>
class ObjectPool {
    std::vector<T> pool_;
    std::vector<T*> free_list_;
    std::mutex mutex_;
    
public:
    T* acquire() {
        std::lock_guard<std::mutex> lock(mutex_);
        if (free_list_.empty()) {
            pool_.emplace_back();
            return &pool_.back();
        }
        T* obj = free_list_.back();
        free_list_.pop_back();
        return obj;
    }
    
    void release(T* obj) {
        std::lock_guard<std::mutex> lock(mutex_);
        free_list_.push_back(obj);
    }
};

// 无锁SPSC队列 —— 低延迟策略中广泛使用
#include <atomic>

template<typename T, size_t Size>
class LockFreeSPSCQueue {
    std::array<T, Size> buffer_;
    std::atomic<size_t> write_pos_{0};
    std::atomic<size_t> read_pos_{0};
    
public:
    bool try_push(const T& item) {
        size_t w = write_pos_.load(std::memory_order_relaxed);
        size_t r = read_pos_.load(std::memory_order_acquire);
        if (w - r >= Size) return false;
        buffer_[w % Size] = item;
        write_pos_.store(w + 1, std::memory_order_release);
        return true;
    }
    
    bool try_pop(T& item) {
        size_t r = read_pos_.load(std::memory_order_relaxed);
        size_t w = write_pos_.load(std::memory_order_acquire);
        if (r >= w) return false;
        item = buffer_[r % Size];
        read_pos_.store(r + 1, std::memory_order_release);
        return true;
    }
};

import numpy as np
import pandas as pd

def compute_factor_vectorized(df):
    """
    用向量化操作替代循环 —— QD的基本素养
    计算一个复合技术因子
    """
    # 价格动量（向量化）
    df['momentum_20'] = df['close'] / df['close'].shift(20) - 1
    
    # 波动率调整（向量化）
    df['vol_20'] = df['returns'].rolling(20).std()
    df['momentum_vol_adj'] = df['momentum_20'] / df['vol_20']
    
    # 成交量确认（向量化）
    df['volume_ratio'] = df['volume'] / df['volume'].rolling(20).mean()
    
    # 综合信号
    df['signal'] = np.where(
        (df['momentum_vol_adj'] > 0.5) & (df['volume_ratio'] > 1.2),
        1,  # 做多
        np.where(df['momentum_vol_adj'] < -0.5, -1, 0)  # 做空或空仓
    )
    
    return df

class BacktestEngine:
    """
    一个简化版的事件驱动回测引擎
    """
    def __init__(self, initial_capital=1_000_000):
        self.capital = initial_capital
        self.positions = {}
        self.trades = []
        self.equity_curve = []
        self.event_queue = []
    
    def on_bar(self, bar_data):
        """每个K线触发"""
        # 1. 更新持仓市值
        self._mark_to_market(bar_data)
        
        # 2. 生成交易信号
        signals = self.strategy.generate_signals(bar_data)
        
        # 3. 订单管理
        for signal in signals:
            self._execute_order(signal, bar_data)
        
        # 4. 记录权益曲线
        self.equity_curve.append({
            'timestamp': bar_data.timestamp,
            'equity': self._total_equity(),
            'cash': self.capital
        })
    
    def run(self, strategy, data_feed):
        """运行完整回测"""
        self.strategy = strategy
        for bar in data_feed:
            self.on_bar(bar)
        return self.generate_report()

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, window, avg

def build_factor_pipeline():
    """
    基于Spark的因子计算管道
    从原始Tick数据到日频因子
    """
    spark = SparkSession.builder \
        .appName("FactorPipeline") \
        .config("spark.sql.adaptive.enabled", "true") \
        .getOrCreate()
    
    # 读取Tick数据
    tick_df = spark.read.parquet("hdfs://data/tick/")
    
    # 聚合为分钟K线
    minute_bars = tick_df.groupBy(
        "symbol", 
        window("timestamp", "1 minute")
    ).agg(
        col("price").first().alias("open"),
        col("price").max().alias("high"),
        col("price").min().alias("low"),
        col("price").last().alias("close"),
        col("volume").sum().alias("volume"),
        col("amount").sum().alias("amount")
    )
    
    # 计算因子
    from pyspark.sql.window import Window
    w = Window.partitionBy("symbol").orderBy("window.start")
    
    factor_df = minute_bars.withColumn(
        "vwap", 
        (col("amount") / col("volume")).over(w)
    ).withColumn(
        "momentum_20",
        col("close") / col("close").shift(20).over(w) - 1
    )
    
    return factor_df

from prometheus_client import Counter, Histogram, Gauge

# 订单延迟
order_latency = Histogram(
    'order_latency_microseconds',
    'Order round-trip latency',
    buckets=[10, 50, 100, 200, 500, 1000, 5000]
)

# 滑点
slippage_gauge = Gauge(
    'slippage_bps',
    'Execution slippage in basis points',
    ['symbol']
)

# 持仓偏差
position_exposure = Gauge(
    'position_exposure',
    'Current position exposure',
    ['symbol', 'side']
)

@order_latency.time()
def send_order(order):
    return exchange.place_order(order)