向量化回测核心
Vectorized Backtest Core
题目详情
回测使用历史数据模拟交易策略，在投入资本前评估潜在风险和盈利。回测的关键要求是消除前瞻偏差：确保策略在任意时刻的决策仅依赖截至该时刻的数据。
任务：实现函数 solution(prices, signal)，计算回测绩效指标。signal 数组中 1 表示买入、-1 表示卖出、0 表示无操作。逐日计算持仓变化和盈亏，返回累计收益序列。必须严格避免前瞻偏差。
英文原题
Backtesting simulates a trading strategy using historical data to evaluate potential risk and profitability before capital deployment. A critical requirement for any backtest is the elimination of look-ahead bias, ensuring that strategy decisions at any given time rely solely on data available up to that moment.
Task
Implement a function solution(prices, signal) that calculates the performance metrics of a trading strategy based on a series of asset prices and trading signals. The function must
解析
问题分析
Backtesting simulates a trading strategy using historical data to evaluate potential risk and profitability before capital deployment. A critical requirement for any backtest is the elimination of look-ahead bias, ensuring that strategy decisions at any given time rely solely on data available up to
解法
根据题目要求实现相应功能。核心逻辑需要：
// 核心数据结构和方法——根据题目 API 约定实现
// 1. 确定状态表示——选择支持所需操作的数据结构
// 2. 实现核心算法——确保 O(·) 时间复杂度和正确性
// 3. 处理边界条件——空输入、极值参数、并发访问验证
用具体输入验证：构造已知输入的测试用例，确认输出匹配预期结果。
复杂度与边界
- 时间复杂度：取决于选用的算法
- 空间复杂度：取决于数据规模
- 关键边界条件：空输入、极值参数、并发场景下的正确性保证
英文解析
Analysis
Backtesting simulates a trading strategy using historical data to assess potential risk and profitability before capital deployment. A critical requirement for any backtest is the elimination of look-ahead bias, ensuring that strategy decisions at any given time rely solely on data available up to that point. Vectorized backtesting processes entire price series as arrays rather than iterating bar-by-bar, enabling efficient computation of signals, positions, and PnL across thousands of bars simultaneously.
Solution
struct BacktestResult { double total_return, sharpe, max_dd; int num_trades; };
class VectorizedBacktester {
std::vector<double> prices_, signals_;
public:
BacktestResult run(const Strategy& strat, const std::vector<double>& prices) {
signals_ = strat.generate(prices); // Vectorized signal generation
std::vector<double> positions_(prices.size());
std::vector<double> equity(prices.size());
equity[0] = 1.0;
for (size_t i = 1; i < prices.size(); ++i) {
positions_[i] = signals_[i-1]; // No look-ahead: use previous signal
double pnl = positions_[i-1] * (prices[i] - prices[i-1]) / prices[i-1];
equity[i] = equity[i-1] * (1.0 + pnl);
}
return computeStats(equity);
}
};Complexity & Edge Cases
- Time complexity: O(N) for signal generation and PnL computation
- Space complexity: O(N) for signal and equity arrays
- Edge cases: (1) Look-ahead bias: signals must use data available at time i-1 only (2) Survivorship bias: only currently-listed stocks included (3) Transaction costs must be deducted per trade
Verification
Run backtest on known price series with deterministic strategy. Verify no look-ahead by checking signal[i] depends only on prices[0..i-1]. Benchmark results against bar-by-bar simulation for consistency.
Key Considerations
Vectorized backtesting trades memory for speed - processing entire arrays at once is 10-100x faster than bar-by-bar iteration. The critical correctness constraint is strict temporal ordering: signal generation and position sizing must never use future data. Transaction cost modeling (spread + impact) must be included to prevent inflated backtest returns.