{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Synthetic Model & Backtest Audit Notebook\n", "\n", "This public notebook is an illustrative StatGazer artifact on synthetic data. It shows the expected structure of a reproducible review without exposing client data, holdings, code, or strategy details.\n", "\n", "Scope shown here: generate a synthetic return series, construct a deliberately flawed signal, test for look-ahead leakage, run a walk-forward split, and produce a remediation checklist." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Setup\n", "\n", "The notebook keeps randomness explicit and uses only synthetic data. A real engagement would replace this block with the client's data-loading contract, data dictionary, and lineage checks." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "RNG_SEED = 20260609\n", "rng = np.random.default_rng(RNG_SEED)\n", "\n", "n = 900\n", "dates = pd.bdate_range(\"2021-01-04\", periods=n)\n", "market = rng.normal(0.0002, 0.0100, n)\n", "idiosyncratic = rng.normal(0.0, 0.0060, n)\n", "returns = 0.35 * market + idiosyncratic\n", "\n", "df = pd.DataFrame({\"market_ret\": market, \"asset_ret\": returns}, index=dates)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Candidate Signal\n", "\n", "This example intentionally includes one leaking feature: a rolling mean that uses future returns. The point is to demonstrate how the audit surfaces the issue, not to endorse the signal." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df[\"momentum_20\"] = df[\"asset_ret\"].rolling(20).mean().shift(1)\n", "df[\"leaking_future_mean_5\"] = df[\"asset_ret\"].shift(-1).rolling(5).mean()\n", "df[\"signal_clean\"] = np.sign(df[\"momentum_20\"]).fillna(0)\n", "df[\"signal_leaky\"] = np.sign(df[\"leaking_future_mean_5\"]).fillna(0)\n", "df[[\"asset_ret\", \"momentum_20\", \"leaking_future_mean_5\", \"signal_clean\", \"signal_leaky\"]].tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Leakage Checks\n", "\n", "A reviewer should be able to trace every feature to the information set available at decision time. This simple check flags columns whose construction uses a negative shift or otherwise depends on future observations." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "feature_contract = pd.DataFrame(\n", " [\n", " {\"feature\": \"momentum_20\", \"available_at_trade_time\": True, \"notes\": \"20-day rolling mean shifted by one day.\"},\n", " {\"feature\": \"leaking_future_mean_5\", \"available_at_trade_time\": False, \"notes\": \"Uses shift(-1); future information enters the feature.\"},\n", " ]\n", ")\n", "\n", "feature_contract" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Backtest Comparison\n", "\n", "The leaky signal should look suspiciously strong. A clean review compares it with a point-in-time-safe baseline and reports the difference plainly." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def summarize_strategy(signal_col: str) -> pd.Series:\n", " strat_ret = df[signal_col].shift(1).fillna(0) * df[\"asset_ret\"]\n", " ann_ret = strat_ret.mean() * 252\n", " ann_vol = strat_ret.std(ddof=0) * np.sqrt(252)\n", " sharpe = ann_ret / ann_vol if ann_vol else np.nan\n", " max_dd = (strat_ret.cumsum() - strat_ret.cumsum().cummax()).min()\n", " return pd.Series({\"annual_return\": ann_ret, \"annual_vol\": ann_vol, \"sharpe\": sharpe, \"max_drawdown_log\": max_dd})\n", "\n", "summary = pd.concat(\n", " {\n", " \"clean_signal\": summarize_strategy(\"signal_clean\"),\n", " \"leaky_signal\": summarize_strategy(\"signal_leaky\"),\n", " },\n", " axis=1,\n", ").T\n", "summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Walk-forward Split\n", "\n", "The notebook records the split explicitly. In a real review, the split logic would be agreed in scope and reproduced from raw data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "split_date = df.index[int(len(df) * 0.7)]\n", "train = df.loc[:split_date].copy()\n", "test = df.loc[split_date:].copy()\n", "\n", "pd.DataFrame(\n", " {\n", " \"period\": [\"train\", \"test\"],\n", " \"start\": [train.index.min().date(), test.index.min().date()],\n", " \"end\": [train.index.max().date(), test.index.max().date()],\n", " \"rows\": [len(train), len(test)],\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Findings\n", "\n", "| Finding | Severity | Evidence | Recommended action |\n", "|---|---:|---|---|\n", "| Future information enters `leaking_future_mean_5` | High | Feature contract marks it unavailable at trade time | Remove feature; rebuild from point-in-time inputs only |\n", "| Validation needs explicit walk-forward design | Medium | Split is present here but not enough for model selection | Define folds before model selection and record them in scope |\n", "| Deliverable needs reviewer-facing limitations | Medium | Metrics alone do not explain where the result can fail | Add limitations and monitoring triggers to memo |\n", "\n", "This notebook is not an investment recommendation, financial-statement audit opinion, or regulatory assurance report. It is an example of a technical model-review artifact." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 5 }