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中文题目A PM is backtesting a signal-driven strategy and also wants to value a stop-loss overlay that behaves like an option. Which measure should dominate each part of the workflow?
打开 →Why is ES harder to backtest directly than VaR in day-to-day risk control?
打开 →A PM deck says: ‘The event-study p-value is 0.03, so there is a 97% probability the signal is real.’ What is the statistical mistake?
打开 →Why can a period of forward paper trading add more evidence than squeezing one more clever slice out of the historical sample?
打开 →Why would it be a category error to judge a forecasting model's backtest solely against risk-neutral densities implied by option prices?
打开 →A daily factor is z-scored using the full-sample mean and standard deviation before the backtest is run. What is the main problem?
打开 →A backtest ranks stocks each month using the latest fully restated accounting data available today, even for years long ago. What is wrong with that setup?
打开 →For a monthly equity backtest, which universe construction is least survivorship-biased? A. Use today’s index constituents for the entire historical sample. B. Use monthly historical constituents and keep delisted names in the panel until their actual exit dates. C. Keep only st
打开 →Why does changing slippage curves, fee schedules, or borrow assumptions after seeing backtest performance count as extra model search?
打开 →Why does backtest overfit become especially dangerous for very high-turnover strategies?
打开 →Why should a PM expect live performance to come in below the very best backtest rather than treat any shortfall as an implementation surprise?
打开 →Why is a broad plateau of good parameter values often more convincing than one spectacularly sharp optimum in a backtest heatmap?
打开 →Why is it dangerous to retire a strategy after disappointment and later relaunch a close cousin because a refreshed backtest looks strong again on overlapping history?
打开 →Why does picking a stop-loss threshold after looking at the full historical equity curve count as backtest search rather than risk management hygiene?
打开 →backtest · backtest-engine · vectorized-backtest · event-driven-backtest · look-ahead-bias · point-in-time · engine-architecture · fill-simulator
打开 →machine-learning · financial-ml · cross-validation · purged-cv · cpcv · deflated-sharpe · multiple-testing · backtest-overfitting
打开 →某周四 早上，上海 某 量化 私募 的 投决会。L1 L3 全部 走 完 的 5 日 动量 策略 摆 在 Confluence 上：事件驱动 引擎、十 项 真实性 清单 全 绿、deflated Sharpe 0.8、PBO 0.35。研究员 问 投资 总监：「什么时候 上 实盘？」投资 总监 不 回答 这 个 问题。她 连 问 四 个 反 问 题。 十 节...
打开 →某周五下午，深圳某 量化 私募 的 风控 周会。一位 研究员 端着一份 价值 动量 复合 策略 的 回测 报告 进 会议室：L1 都做对了——事件驱动 引擎、信号 计算 处处 .shift(1) 纪律。在 沪深300 成分股 上 2014 2023 回测，年化 夏普比率 1.3，曲线 干净、可上线。风控 总监 不问 信号本身 的 任何 一个 字，连珠炮 问了...
打开 →某周二，上海某 量化 私募 的策略评审会上。一位 研究员 把 5 日 动量 信号 的回测报告投到屏幕上：在 沪深300 ETF 510300 上从 2014 01 01 到 2023 12 31 的回测，扣费后年化 夏普比率 1.8。曲线穿过 2015 股灾、穿过 2018 中美贸易摩擦、穿过 2022 疫情 + 房地产 双杀，姿态优雅。投资 决策 委员会 ...
打开 →某周三 下午，上海 量化 私募 明汯 / 幻方 风格 的 投决会。研究员 上 来 一个 动量 策略：L1 引擎 是 事件驱动（干净）；L2 真实性 清单 每 一 项 都 过（PIT 数据、survivorship free 沪深300 股票池、下根 K 线 开盘 成交、双边 10 bps 成本、不 做 空）。报告 的 夏普比率 在 2014 2023 上 是...
打开 →中信 CITIC 算法交易部一位资深执行交易员，正在与一家中型量化私募的投资经理通电话。私募需要在收盘前 30 分钟清掉 100 万股 600519 贵州茅台。到达价 RMB 1800.00；距离收盘 30 分钟。投资经理要求订单完成。交易员冷静地解释：「直接打盘口市价单，意味着接下来 30 秒 100% 参与率， 250 bp 冲击。30 分钟内做 TWA...
打开 →钩子:五十条弱 alpha 与一个总组合 你在一家中证500 中频量化私募(private fund)工作。研究团队在过去六个月里训练出了五十条独立的 ML alpha:有用 LightGBM 在 沪深300 / 中证500 因子风格暴露上做次日 alpha 的,有 1 D CNN 在分钟线上做日内动量(momentum)的,有 Transformer 在卖...
打开 →A live manager panel shows 27 low-leverage funds and 18 high-leverage funds. Survival rates for those groups were 90% and 60%, respectively. Suppose low-leverage funds average 1.2x gross leverage and high-leverage funds average 2.4x gross leverage. What was the average gross lev
打开 →A vendor offers two hedge-fund datasets. Dataset A contains only funds that are currently reporting, but it includes long backfilled histories for those funds. Dataset B stores monthly reporting snapshots and preserves closed funds in the historical archive. Which dataset is be
打开 →Suppose 50 genuinely null standardized t-statistics are approximately independent N(0,1). What is the probability the largest of them exceeds 2.4?
打开 →A researcher wants the 2016-2025 average return of all funds launched in 2016. Which data pull is least exposed to survivorship bias? A. Keep only funds that are still alive in 2025, then use their full back history. B. Keep funds alive on each evaluation date, but retroactively
打开 →A live database shows 28 small-capacity funds and 42 large-capacity funds. Survival rates for those groups were 40% and 70%, respectively. Suppose small-capacity funds average $1.0$ billion of capacity and large-capacity funds average $3.0$ billion. By how much does the displaye
打开 →A live fund panel shows 45 low-turnover funds and 15 high-turnover funds. Historical survival rates were 90% for low-turnover funds and 30% for high-turnover funds. By how many percentage points does the displayed live panel understate the original share of high-turnover launche
打开 →A research platform runs 200 null strategies. Only strategies with in-sample p-value below 15% are promoted, and each promoted strategy must then pass a fresh 5% confirmation test. Assuming independence under the null, what is the expected number of false strategies that survive
打开 →You test 40 independent noise strategies. A strategy is launched only if it passes an in-sample screen at level 1/20 and then passes a second independent validation at level 1/10. Under the global null, what is the probability that at least one noise strategy still gets launched?
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