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Add notes, code, tests, and chapter on effective spread🍕 (#184)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,133 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "import numpy.typing as npt\n", | ||
| "from sklearn.metrics import make_scorer\n", | ||
| "from sklearn.utils import check_consistent_length" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def effective_spread(\n", | ||
| " y_pred: npt.NDArray, trade_price: npt.NDArray, fundamental_value: npt.NDArray\n", | ||
| ") -> np.float64:\n", | ||
| " \"\"\"\n", | ||
| " Calculate the effective spread given by:\n", | ||
| " $$\n", | ||
| " S_{i,t} = 2 (P_{i,t} - V_{i,t}) D_{i,t}\n", | ||
| " $$\n", | ||
| "\n", | ||
| " Args:\n", | ||
| " y_pred (npt.NDArray): indicator if the trade is a buy or sell\n", | ||
| " trade_price (npt.NDArray): trade price\n", | ||
| " fundamental_value (npt.NDArray): fundamental value e. g., bid-ask midpoint.\n", | ||
| " Returns:\n", | ||
| " float: average effective spread\n", | ||
| " \"\"\"\n", | ||
| " check_consistent_length(y_pred, trade_price, fundamental_value)\n", | ||
| " return np.mean(2 * (trade_price - fundamental_value) * y_pred)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "y_pred = np.random.choice([-1,1], size=(10))\n", | ||
| "trade_price = np.random.rand(10) * 100\n", | ||
| "fundamental_value = np.random.rand(10) * 100\n", | ||
| "\n", | ||
| "eff_sp = effective_spread(y_pred, trade_price, fundamental_value)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 10, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "array([-83.57555436, -94.25680187, 92.10142797, 41.24263376,\n", | ||
| " 168.57843754, 94.69759222, 39.67382461, 81.81819241,\n", | ||
| " 135.25950003, -79.62206844])" | ||
| ] | ||
| }, | ||
| "execution_count": 10, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "2* (trade_price - fundamental_value) * y_pred" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "39.591718386351225" | ||
| ] | ||
| }, | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "eff_sp" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "score = make_scorer(effective_spread, greater_is_better=True)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
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| "file_extension": ".py", | ||
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| } |
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