1311 lines
667 KiB
Plaintext
1311 lines
667 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Datos reales de Binance\n",
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"\n",
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"Usamos la API pública de Binance (gratis, sin API key) para obtener:\n",
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"1. **Order book** (L2) — profundidad del libro en tiempo real\n",
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"2. **Trades recientes** — los últimos fills ejecutados\n",
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"3. **OHLCV** — velas históricas\n",
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"\n",
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"Después aplicamos las mismas técnicas de estimación del notebook 03 sobre datos reales."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Exchange: Binance\n",
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"Rate limit: 50ms\n"
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]
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}
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],
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"source": [
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"import ccxt\n",
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"import polars as pl\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from datetime import datetime, timedelta\n",
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"import time\n",
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"\n",
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"exchange = ccxt.binance({'enableRateLimit': True})\n",
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"print(f\"Exchange: {exchange.name}\")\n",
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"print(f\"Rate limit: {exchange.rateLimit}ms\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Elegir par y explorar qué hay disponible"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Par: BTC/USDT\n",
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"Último precio: 66759.2\n",
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"Bid: 66759.19 Ask: 66759.2\n",
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"Spread: 0.01 (0.0000%)\n",
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"Volumen 24h: 15,644 BTC\n",
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"Volumen 24h: $1,045,474,882 USDT\n"
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]
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}
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],
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"source": [
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"SYMBOL = 'BTC/USDT'\n",
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"\n",
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"ticker = exchange.fetch_ticker(SYMBOL)\n",
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"print(f\"Par: {SYMBOL}\")\n",
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"print(f\"Último precio: {ticker['last']}\")\n",
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"print(f\"Bid: {ticker['bid']} Ask: {ticker['ask']}\")\n",
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"print(f\"Spread: {ticker['ask'] - ticker['bid']:.2f} ({(ticker['ask'] - ticker['bid']) / ticker['last'] * 100:.4f}%)\")\n",
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"print(f\"Volumen 24h: {ticker['baseVolume']:,.0f} BTC\")\n",
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"print(f\"Volumen 24h: ${ticker['quoteVolume']:,.0f} USDT\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Order Book (L2)\n",
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"\n",
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"El order book de Binance te da los **niveles de precio agregados** — no ves órdenes individuales (no es L3).\n",
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"Cada nivel muestra: precio y cantidad total a ese precio."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "unsupported operand type(s) for /: 'NoneType' and 'int'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m df, ob[\u001b[33m'timestamp'\u001b[39m]\n\u001b[32m 13\u001b[39m \n\u001b[32m 14\u001b[39m \n\u001b[32m 15\u001b[39m ob_df, ob_ts = fetch_orderbook(SYMBOL, limit=\u001b[32m20\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m print(f\"Timestamp: {datetime.fromtimestamp(ob_ts/\u001b[32m1000\u001b[39m)}\")\n\u001b[32m 17\u001b[39m print(f\"\\nTop 5 bids:\")\n\u001b[32m 18\u001b[39m print(ob_df.filter(pl.col(\u001b[33m'side'\u001b[39m) == \u001b[33m'bid'\u001b[39m).head(\u001b[32m5\u001b[39m))\n\u001b[32m 19\u001b[39m print(f\"\\nTop 5 asks:\")\n",
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"\u001b[31mTypeError\u001b[39m: unsupported operand type(s) for /: 'NoneType' and 'int'"
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]
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}
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],
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"source": [
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"def fetch_orderbook(symbol: str, limit: int = 50) -> pl.DataFrame:\n",
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" \"\"\"Obtiene el order book y lo devuelve como DataFrame.\"\"\"\n",
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" ob = exchange.fetch_order_book(symbol, limit=limit)\n",
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"\n",
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" bids = pl.DataFrame(ob['bids'], schema=['price', 'qty'], orient='row')\n",
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" bids = bids.with_columns(pl.lit('bid').alias('side'))\n",
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"\n",
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" asks = pl.DataFrame(ob['asks'], schema=['price', 'qty'], orient='row')\n",
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" asks = asks.with_columns(pl.lit('ask').alias('side'))\n",
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"\n",
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" df = pl.concat([bids, asks])\n",
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" return df, ob['timestamp']\n",
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"\n",
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"\n",
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"ob_df, ob_ts = fetch_orderbook(SYMBOL, limit=20)\n",
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"print(f\"Timestamp: {datetime.fromtimestamp(ob_ts/1000)}\")\n",
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"print(f\"\\nTop 5 bids:\")\n",
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"print(ob_df.filter(pl.col('side') == 'bid').head(5))\n",
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"print(f\"\\nTop 5 asks:\")\n",
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"print(ob_df.filter(pl.col('side') == 'ask').head(5))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "\nBTC/USDT Order Book — Spread: $0.01 (0.0000%) — Mid: $66,719.92\n ^\nParseException: Expected end of text, found '$' (at char 30), (line:1, col:31)",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 30\u001b[39m\n\u001b[32m 26\u001b[39m plt.tight_layout()\n\u001b[32m 27\u001b[39m plt.show()\n\u001b[32m 28\u001b[39m \n\u001b[32m 29\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m plot_real_orderbook(ob_df, SYMBOL)\n",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 26\u001b[39m, in \u001b[36mplot_real_orderbook\u001b[39m\u001b[34m(ob_df, symbol)\u001b[39m\n\u001b[32m 22\u001b[39m ax.axvline(x=mid, color=\u001b[33m'gray'\u001b[39m, linestyle=\u001b[33m'--'\u001b[39m, linewidth=\u001b[32m0.8\u001b[39m)\n\u001b[32m 23\u001b[39m ax.set_title(f'{symbol} Order Book — Spread: ${spread:.2f} ({spread/mid*\u001b[32m100\u001b[39m:.4f}%) — Mid: ${mid:,.2f}')\n\u001b[32m 24\u001b[39m ax.legend()\n\u001b[32m 25\u001b[39m ax.grid(\u001b[38;5;28;01mTrue\u001b[39;00m, alpha=\u001b[32m0.3\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m plt.tight_layout()\n\u001b[32m 27\u001b[39m plt.show()\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/pyplot.py:2843\u001b[39m, in \u001b[36mtight_layout\u001b[39m\u001b[34m(pad, h_pad, w_pad, rect)\u001b[39m\n\u001b[32m 2835\u001b[39m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Figure.tight_layout)\n\u001b[32m 2836\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mtight_layout\u001b[39m(\n\u001b[32m 2837\u001b[39m *,\n\u001b[32m (...)\u001b[39m\u001b[32m 2841\u001b[39m rect: \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mfloat\u001b[39m] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 2842\u001b[39m ) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2843\u001b[39m \u001b[43mgcf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtight_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mh_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mw_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrect\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrect\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/figure.py:3640\u001b[39m, in \u001b[36mFigure.tight_layout\u001b[39m\u001b[34m(self, pad, h_pad, w_pad, rect)\u001b[39m\n\u001b[32m 3638\u001b[39m previous_engine = \u001b[38;5;28mself\u001b[39m.get_layout_engine()\n\u001b[32m 3639\u001b[39m \u001b[38;5;28mself\u001b[39m.set_layout_engine(engine)\n\u001b[32m-> \u001b[39m\u001b[32m3640\u001b[39m \u001b[43mengine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 3641\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m previous_engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 3642\u001b[39m previous_engine, (TightLayoutEngine, PlaceHolderLayoutEngine)\n\u001b[32m 3643\u001b[39m ):\n\u001b[32m 3644\u001b[39m _api.warn_external(\u001b[33m'\u001b[39m\u001b[33mThe figure layout has changed to tight\u001b[39m\u001b[33m'\u001b[39m)\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/layout_engine.py:188\u001b[39m, in \u001b[36mTightLayoutEngine.execute\u001b[39m\u001b[34m(self, fig)\u001b[39m\n\u001b[32m 186\u001b[39m renderer = fig._get_renderer()\n\u001b[32m 187\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m--> \u001b[39m\u001b[32m188\u001b[39m kwargs = \u001b[43mget_tight_layout_figure\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 189\u001b[39m \u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_subplotspec_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 190\u001b[39m \u001b[43m \u001b[49m\u001b[43mpad\u001b[49m\u001b[43m=\u001b[49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpad\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[43m=\u001b[49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mh_pad\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[43m=\u001b[49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mw_pad\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 191\u001b[39m \u001b[43m \u001b[49m\u001b[43mrect\u001b[49m\u001b[43m=\u001b[49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrect\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 192\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m kwargs:\n\u001b[32m 193\u001b[39m fig.subplots_adjust(**kwargs)\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/_tight_layout.py:266\u001b[39m, in \u001b[36mget_tight_layout_figure\u001b[39m\u001b[34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[39m\n\u001b[32m 261\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {}\n\u001b[32m 262\u001b[39m span_pairs.append((\n\u001b[32m 263\u001b[39m \u001b[38;5;28mslice\u001b[39m(ss.rowspan.start * div_row, ss.rowspan.stop * div_row),\n\u001b[32m 264\u001b[39m \u001b[38;5;28mslice\u001b[39m(ss.colspan.start * div_col, ss.colspan.stop * div_col)))\n\u001b[32m--> \u001b[39m\u001b[32m266\u001b[39m kwargs = \u001b[43m_auto_adjust_subplotpars\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 267\u001b[39m \u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_nrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_ncols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 268\u001b[39m \u001b[43m \u001b[49m\u001b[43mspan_pairs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspan_pairs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 269\u001b[39m \u001b[43m \u001b[49m\u001b[43msubplot_list\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubplot_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 270\u001b[39m \u001b[43m \u001b[49m\u001b[43max_bbox_list\u001b[49m\u001b[43m=\u001b[49m\u001b[43max_bbox_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 271\u001b[39m \u001b[43m \u001b[49m\u001b[43mpad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mh_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mw_pad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 273\u001b[39m \u001b[38;5;66;03m# kwargs can be none if tight_layout fails...\u001b[39;00m\n\u001b[32m 274\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m rect \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 275\u001b[39m \u001b[38;5;66;03m# if rect is given, the whole subplots area (including\u001b[39;00m\n\u001b[32m 276\u001b[39m \u001b[38;5;66;03m# labels) will fit into the rect instead of the\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 280\u001b[39m \u001b[38;5;66;03m# auto_adjust_subplotpars twice, where the second run\u001b[39;00m\n\u001b[32m 281\u001b[39m \u001b[38;5;66;03m# with adjusted rect parameters.\u001b[39;00m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/_tight_layout.py:82\u001b[39m, in \u001b[36m_auto_adjust_subplotpars\u001b[39m\u001b[34m(fig, renderer, shape, span_pairs, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001b[39m\n\u001b[32m 80\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m subplots:\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ax.get_visible():\n\u001b[32m---> \u001b[39m\u001b[32m82\u001b[39m bb += [\u001b[43mmartist\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_tightbbox_for_layout_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[32m 84\u001b[39m tight_bbox_raw = Bbox.union(bb)\n\u001b[32m 85\u001b[39m tight_bbox = fig.transFigure.inverted().transform_bbox(tight_bbox_raw)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:1402\u001b[39m, in \u001b[36m_get_tightbbox_for_layout_only\u001b[39m\u001b[34m(obj, *args, **kwargs)\u001b[39m\n\u001b[32m 1396\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1397\u001b[39m \u001b[33;03mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[32m 1398\u001b[39m \u001b[33;03m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[32m 1399\u001b[39m \u001b[33;03mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[32m 1400\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1401\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfor_layout_only\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1403\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m 1404\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj.get_tightbbox(*args, **kwargs)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/axes/_base.py:4567\u001b[39m, in \u001b[36m_AxesBase.get_tightbbox\u001b[39m\u001b[34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[39m\n\u001b[32m 4565\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ba:\n\u001b[32m 4566\u001b[39m bb.append(ba)\n\u001b[32m-> \u001b[39m\u001b[32m4567\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4568\u001b[39m axbbox = \u001b[38;5;28mself\u001b[39m.get_window_extent(renderer)\n\u001b[32m 4569\u001b[39m bb.append(axbbox)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/axes/_base.py:3144\u001b[39m, in \u001b[36m_AxesBase._update_title_position\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3142\u001b[39m _log.debug(\u001b[33m'\u001b[39m\u001b[33mtop of Axes not in the figure, so title not moved\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3143\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3144\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtitle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m.ymin < top:\n\u001b[32m 3145\u001b[39m _, y = \u001b[38;5;28mself\u001b[39m.transAxes.inverted().transform((\u001b[32m0\u001b[39m, top))\n\u001b[32m 3146\u001b[39m title.set_position((x, y))\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:969\u001b[39m, in \u001b[36mText.get_window_extent\u001b[39m\u001b[34m(self, renderer, dpi)\u001b[39m\n\u001b[32m 964\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 965\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 966\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mwant to call \u001b[39m\u001b[33m'\u001b[39m\u001b[33mfigure.draw_without_rendering()\u001b[39m\u001b[33m'\u001b[39m\u001b[33m first.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 968\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m cbook._setattr_cm(fig, dpi=dpi):\n\u001b[32m--> \u001b[39m\u001b[32m969\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 970\u001b[39m x, y = \u001b[38;5;28mself\u001b[39m.get_unitless_position()\n\u001b[32m 971\u001b[39m x, y = \u001b[38;5;28mself\u001b[39m.get_transform().transform((x, y))\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:382\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 380\u001b[39m clean_line, ismath = \u001b[38;5;28mself\u001b[39m._preprocess_math(line)\n\u001b[32m 381\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m w, h, d = \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 386\u001b[39m w = h = d = \u001b[32m0\u001b[39m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:69\u001b[39m, in \u001b[36m_get_text_metrics_with_cache\u001b[39m\u001b[34m(renderer, text, fontprop, ismath, dpi)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[32m 67\u001b[39m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 70\u001b[39m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:77\u001b[39m, in \u001b[36m_get_text_metrics_with_cache_impl\u001b[39m\u001b[34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[39m\n\u001b[32m 73\u001b[39m \u001b[38;5;129m@functools\u001b[39m.lru_cache(\u001b[32m4096\u001b[39m)\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[32m 75\u001b[39m renderer_ref, text, fontprop, ismath, dpi):\n\u001b[32m 76\u001b[39m \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/backends/backend_agg.py:215\u001b[39m, in \u001b[36mRendererAgg.get_text_width_height_descent\u001b[39m\u001b[34m(self, s, prop, ismath)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().get_text_width_height_descent(s, prop, ismath)\n\u001b[32m 213\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[32m 214\u001b[39m ox, oy, width, height, descent, font_image = \\\n\u001b[32m--> \u001b[39m\u001b[32m215\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmathtext_parser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[32m 218\u001b[39m font = \u001b[38;5;28mself\u001b[39m._prepare_font(prop)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/mathtext.py:86\u001b[39m, in \u001b[36mMathTextParser.parse\u001b[39m\u001b[34m(self, s, dpi, prop, antialiased)\u001b[39m\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n\u001b[32m 82\u001b[39m load_glyph_flags = {\n\u001b[32m 83\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mvector\u001b[39m\u001b[33m\"\u001b[39m: LoadFlags.NO_HINTING,\n\u001b[32m 84\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mraster\u001b[39m\u001b[33m\"\u001b[39m: backend_agg.get_hinting_flag(),\n\u001b[32m 85\u001b[39m }[\u001b[38;5;28mself\u001b[39m._output_type]\n\u001b[32m---> \u001b[39m\u001b[32m86\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialiased\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mload_glyph_flags\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/mathtext.py:100\u001b[39m, in \u001b[36mMathTextParser._parse_cached\u001b[39m\u001b[34m(self, s, dpi, prop, antialiased, load_glyph_flags)\u001b[39m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[32m 98\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m._parser = _mathtext.Parser()\n\u001b[32m--> \u001b[39m\u001b[32m100\u001b[39m box = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 101\u001b[39m output = _mathtext.ship(box)\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._output_type == \u001b[33m\"\u001b[39m\u001b[33mvector\u001b[39m\u001b[33m\"\u001b[39m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/_mathtext.py:2167\u001b[39m, in \u001b[36mParser.parse\u001b[39m\u001b[34m(self, s, fonts_object, fontsize, dpi)\u001b[39m\n\u001b[32m 2164\u001b[39m result = \u001b[38;5;28mself\u001b[39m._expression.parse_string(s)\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m 2166\u001b[39m \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2167\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m + ParseException.explain(err, \u001b[32m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 2168\u001b[39m \u001b[38;5;28mself\u001b[39m._state_stack = []\n\u001b[32m 2169\u001b[39m \u001b[38;5;28mself\u001b[39m._in_subscript_or_superscript = \u001b[38;5;28;01mFalse\u001b[39;00m\n",
|
||
"\u001b[31mValueError\u001b[39m: \nBTC/USDT Order Book — Spread: $0.01 (0.0000%) — Mid: $66,719.92\n ^\nParseException: Expected end of text, found '$' (at char 30), (line:1, col:31)"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Error in callback <function _draw_all_if_interactive at 0x7f453d2f0040> (for post_execute), with arguments args (),kwargs {}:\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "ValueError",
|
||
"evalue": "\nBTC/USDT Order Book — Spread: $0.01 (0.0000%) — Mid: $66,719.92\n ^\nParseException: Expected end of text, found '$' (at char 30), (line:1, col:31)",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/pyplot.py:278\u001b[39m, in \u001b[36m_draw_all_if_interactive\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 276\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_draw_all_if_interactive\u001b[39m() -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 277\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m matplotlib.is_interactive():\n\u001b[32m--> \u001b[39m\u001b[32m278\u001b[39m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/_pylab_helpers.py:131\u001b[39m, in \u001b[36mGcf.draw_all\u001b[39m\u001b[34m(cls, force)\u001b[39m\n\u001b[32m 129\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.get_all_fig_managers():\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager.canvas.figure.stale:\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m \u001b[43mmanager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcanvas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/backend_bases.py:1893\u001b[39m, in \u001b[36mFigureCanvasBase.draw_idle\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_idle_drawing:\n\u001b[32m 1892\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._idle_draw_cntx():\n\u001b[32m-> \u001b[39m\u001b[32m1893\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/backends/backend_agg.py:382\u001b[39m, in \u001b[36mFigureCanvasAgg.draw\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 379\u001b[39m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[32m 380\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.toolbar._wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.toolbar\n\u001b[32m 381\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[32m 384\u001b[39m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[32m 385\u001b[39m \u001b[38;5;28msuper\u001b[39m().draw()\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:94\u001b[39m, in \u001b[36m_finalize_rasterization.<locals>.draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 93\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 96\u001b[39m renderer.stop_rasterizing()\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[39m, in \u001b[36mallow_rasterization.<locals>.draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 69\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/figure.py:3257\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3254\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3256\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3257\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3258\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3260\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mfigure\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3261\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 136\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 137\u001b[39m image_group = []\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[39m, in \u001b[36mallow_rasterization.<locals>.draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 69\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/axes/_base.py:3190\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3187\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m spine \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.spines.values():\n\u001b[32m 3188\u001b[39m artists.remove(spine)\n\u001b[32m-> \u001b[39m\u001b[32m3190\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3192\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.axison:\n\u001b[32m 3193\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._axis_map.values():\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/axes/_base.py:3144\u001b[39m, in \u001b[36m_AxesBase._update_title_position\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3142\u001b[39m _log.debug(\u001b[33m'\u001b[39m\u001b[33mtop of Axes not in the figure, so title not moved\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3143\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3144\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtitle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m.ymin < top:\n\u001b[32m 3145\u001b[39m _, y = \u001b[38;5;28mself\u001b[39m.transAxes.inverted().transform((\u001b[32m0\u001b[39m, top))\n\u001b[32m 3146\u001b[39m title.set_position((x, y))\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:969\u001b[39m, in \u001b[36mText.get_window_extent\u001b[39m\u001b[34m(self, renderer, dpi)\u001b[39m\n\u001b[32m 964\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 965\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 966\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mwant to call \u001b[39m\u001b[33m'\u001b[39m\u001b[33mfigure.draw_without_rendering()\u001b[39m\u001b[33m'\u001b[39m\u001b[33m first.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 968\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m cbook._setattr_cm(fig, dpi=dpi):\n\u001b[32m--> \u001b[39m\u001b[32m969\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 970\u001b[39m x, y = \u001b[38;5;28mself\u001b[39m.get_unitless_position()\n\u001b[32m 971\u001b[39m x, y = \u001b[38;5;28mself\u001b[39m.get_transform().transform((x, y))\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:382\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 380\u001b[39m clean_line, ismath = \u001b[38;5;28mself\u001b[39m._preprocess_math(line)\n\u001b[32m 381\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m w, h, d = \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 386\u001b[39m w = h = d = \u001b[32m0\u001b[39m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:69\u001b[39m, in \u001b[36m_get_text_metrics_with_cache\u001b[39m\u001b[34m(renderer, text, fontprop, ismath, dpi)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[32m 67\u001b[39m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 70\u001b[39m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:77\u001b[39m, in \u001b[36m_get_text_metrics_with_cache_impl\u001b[39m\u001b[34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[39m\n\u001b[32m 73\u001b[39m \u001b[38;5;129m@functools\u001b[39m.lru_cache(\u001b[32m4096\u001b[39m)\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[32m 75\u001b[39m renderer_ref, text, fontprop, ismath, dpi):\n\u001b[32m 76\u001b[39m \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/backends/backend_agg.py:215\u001b[39m, in \u001b[36mRendererAgg.get_text_width_height_descent\u001b[39m\u001b[34m(self, s, prop, ismath)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().get_text_width_height_descent(s, prop, ismath)\n\u001b[32m 213\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[32m 214\u001b[39m ox, oy, width, height, descent, font_image = \\\n\u001b[32m--> \u001b[39m\u001b[32m215\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmathtext_parser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[32m 218\u001b[39m font = \u001b[38;5;28mself\u001b[39m._prepare_font(prop)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/mathtext.py:86\u001b[39m, in \u001b[36mMathTextParser.parse\u001b[39m\u001b[34m(self, s, dpi, prop, antialiased)\u001b[39m\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n\u001b[32m 82\u001b[39m load_glyph_flags = {\n\u001b[32m 83\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mvector\u001b[39m\u001b[33m\"\u001b[39m: LoadFlags.NO_HINTING,\n\u001b[32m 84\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mraster\u001b[39m\u001b[33m\"\u001b[39m: backend_agg.get_hinting_flag(),\n\u001b[32m 85\u001b[39m }[\u001b[38;5;28mself\u001b[39m._output_type]\n\u001b[32m---> \u001b[39m\u001b[32m86\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialiased\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mload_glyph_flags\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/mathtext.py:100\u001b[39m, in \u001b[36mMathTextParser._parse_cached\u001b[39m\u001b[34m(self, s, dpi, prop, antialiased, load_glyph_flags)\u001b[39m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[32m 98\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m._parser = _mathtext.Parser()\n\u001b[32m--> \u001b[39m\u001b[32m100\u001b[39m box = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 101\u001b[39m output = _mathtext.ship(box)\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._output_type == \u001b[33m\"\u001b[39m\u001b[33mvector\u001b[39m\u001b[33m\"\u001b[39m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/_mathtext.py:2167\u001b[39m, in \u001b[36mParser.parse\u001b[39m\u001b[34m(self, s, fonts_object, fontsize, dpi)\u001b[39m\n\u001b[32m 2164\u001b[39m result = \u001b[38;5;28mself\u001b[39m._expression.parse_string(s)\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m 2166\u001b[39m \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2167\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m + ParseException.explain(err, \u001b[32m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 2168\u001b[39m \u001b[38;5;28mself\u001b[39m._state_stack = []\n\u001b[32m 2169\u001b[39m \u001b[38;5;28mself\u001b[39m._in_subscript_or_superscript = \u001b[38;5;28;01mFalse\u001b[39;00m\n",
|
||
"\u001b[31mValueError\u001b[39m: \nBTC/USDT Order Book — Spread: $0.01 (0.0000%) — Mid: $66,719.92\n ^\nParseException: Expected end of text, found '$' (at char 30), (line:1, col:31)"
|
||
]
|
||
},
|
||
{
|
||
"ename": "ValueError",
|
||
"evalue": "\nBTC/USDT Order Book — Spread: $0.01 (0.0000%) — Mid: $66,719.92\n ^\nParseException: Expected end of text, found '$' (at char 30), (line:1, col:31)",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/backend_bases.py:2157\u001b[39m, in \u001b[36mFigureCanvasBase.print_figure\u001b[39m\u001b[34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[39m\n\u001b[32m 2154\u001b[39m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[32m 2155\u001b[39m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[32m 2156\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[33m\"\u001b[39m\u001b[33m_draw_disabled\u001b[39m\u001b[33m\"\u001b[39m, nullcontext)():\n\u001b[32m-> \u001b[39m\u001b[32m2157\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2158\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[32m 2159\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches == \u001b[33m\"\u001b[39m\u001b[33mtight\u001b[39m\u001b[33m\"\u001b[39m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:94\u001b[39m, in \u001b[36m_finalize_rasterization.<locals>.draw_wrapper\u001b[39m\u001b[34m(artist, renderer, *args, **kwargs)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[32m 93\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw_wrapper\u001b[39m(artist, renderer, *args, **kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m result = \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m renderer._rasterizing:\n\u001b[32m 96\u001b[39m renderer.stop_rasterizing()\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[39m, in \u001b[36mallow_rasterization.<locals>.draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 69\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/figure.py:3257\u001b[39m, in \u001b[36mFigure.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3254\u001b[39m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[32m 3256\u001b[39m \u001b[38;5;28mself\u001b[39m.patch.draw(renderer)\n\u001b[32m-> \u001b[39m\u001b[32m3257\u001b[39m \u001b[43mmimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3258\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3260\u001b[39m renderer.close_group(\u001b[33m'\u001b[39m\u001b[33mfigure\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3261\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[39m, in \u001b[36m_draw_list_compositing_images\u001b[39m\u001b[34m(renderer, parent, artists, suppress_composite)\u001b[39m\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 136\u001b[39m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[32m 137\u001b[39m image_group = []\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[39m, in \u001b[36mallow_rasterization.<locals>.draw_wrapper\u001b[39m\u001b[34m(artist, renderer)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 69\u001b[39m renderer.start_filter()\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 72\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m artist.get_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/axes/_base.py:3190\u001b[39m, in \u001b[36m_AxesBase.draw\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3187\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m spine \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.spines.values():\n\u001b[32m 3188\u001b[39m artists.remove(spine)\n\u001b[32m-> \u001b[39m\u001b[32m3190\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3192\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.axison:\n\u001b[32m 3193\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._axis_map.values():\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/axes/_base.py:3144\u001b[39m, in \u001b[36m_AxesBase._update_title_position\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 3142\u001b[39m _log.debug(\u001b[33m'\u001b[39m\u001b[33mtop of Axes not in the figure, so title not moved\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 3143\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3144\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtitle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m.ymin < top:\n\u001b[32m 3145\u001b[39m _, y = \u001b[38;5;28mself\u001b[39m.transAxes.inverted().transform((\u001b[32m0\u001b[39m, top))\n\u001b[32m 3146\u001b[39m title.set_position((x, y))\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:969\u001b[39m, in \u001b[36mText.get_window_extent\u001b[39m\u001b[34m(self, renderer, dpi)\u001b[39m\n\u001b[32m 964\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 965\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 966\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mwant to call \u001b[39m\u001b[33m'\u001b[39m\u001b[33mfigure.draw_without_rendering()\u001b[39m\u001b[33m'\u001b[39m\u001b[33m first.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 968\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m cbook._setattr_cm(fig, dpi=dpi):\n\u001b[32m--> \u001b[39m\u001b[32m969\u001b[39m bbox, info, descent = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 970\u001b[39m x, y = \u001b[38;5;28mself\u001b[39m.get_unitless_position()\n\u001b[32m 971\u001b[39m x, y = \u001b[38;5;28mself\u001b[39m.get_transform().transform((x, y))\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:382\u001b[39m, in \u001b[36mText._get_layout\u001b[39m\u001b[34m(self, renderer)\u001b[39m\n\u001b[32m 380\u001b[39m clean_line, ismath = \u001b[38;5;28mself\u001b[39m._preprocess_math(line)\n\u001b[32m 381\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m w, h, d = \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 386\u001b[39m w = h = d = \u001b[32m0\u001b[39m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:69\u001b[39m, in \u001b[36m_get_text_metrics_with_cache\u001b[39m\u001b[34m(renderer, text, fontprop, ismath, dpi)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[32m 67\u001b[39m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 70\u001b[39m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/text.py:77\u001b[39m, in \u001b[36m_get_text_metrics_with_cache_impl\u001b[39m\u001b[34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[39m\n\u001b[32m 73\u001b[39m \u001b[38;5;129m@functools\u001b[39m.lru_cache(\u001b[32m4096\u001b[39m)\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[32m 75\u001b[39m renderer_ref, text, fontprop, ismath, dpi):\n\u001b[32m 76\u001b[39m \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/backends/backend_agg.py:215\u001b[39m, in \u001b[36mRendererAgg.get_text_width_height_descent\u001b[39m\u001b[34m(self, s, prop, ismath)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().get_text_width_height_descent(s, prop, ismath)\n\u001b[32m 213\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[32m 214\u001b[39m ox, oy, width, height, descent, font_image = \\\n\u001b[32m--> \u001b[39m\u001b[32m215\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmathtext_parser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[32m 218\u001b[39m font = \u001b[38;5;28mself\u001b[39m._prepare_font(prop)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/mathtext.py:86\u001b[39m, in \u001b[36mMathTextParser.parse\u001b[39m\u001b[34m(self, s, dpi, prop, antialiased)\u001b[39m\n\u001b[32m 81\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n\u001b[32m 82\u001b[39m load_glyph_flags = {\n\u001b[32m 83\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mvector\u001b[39m\u001b[33m\"\u001b[39m: LoadFlags.NO_HINTING,\n\u001b[32m 84\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mraster\u001b[39m\u001b[33m\"\u001b[39m: backend_agg.get_hinting_flag(),\n\u001b[32m 85\u001b[39m }[\u001b[38;5;28mself\u001b[39m._output_type]\n\u001b[32m---> \u001b[39m\u001b[32m86\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mantialiased\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mload_glyph_flags\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/mathtext.py:100\u001b[39m, in \u001b[36mMathTextParser._parse_cached\u001b[39m\u001b[34m(self, s, dpi, prop, antialiased, load_glyph_flags)\u001b[39m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[32m 98\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m._parser = _mathtext.Parser()\n\u001b[32m--> \u001b[39m\u001b[32m100\u001b[39m box = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parser\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 101\u001b[39m output = _mathtext.ship(box)\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._output_type == \u001b[33m\"\u001b[39m\u001b[33mvector\u001b[39m\u001b[33m\"\u001b[39m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/fn_registry/analysis/estudio_mercados/.venv/lib/python3.13/site-packages/matplotlib/_mathtext.py:2167\u001b[39m, in \u001b[36mParser.parse\u001b[39m\u001b[34m(self, s, fonts_object, fontsize, dpi)\u001b[39m\n\u001b[32m 2164\u001b[39m result = \u001b[38;5;28mself\u001b[39m._expression.parse_string(s)\n\u001b[32m 2165\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m 2166\u001b[39m \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2167\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m + ParseException.explain(err, \u001b[32m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 2168\u001b[39m \u001b[38;5;28mself\u001b[39m._state_stack = []\n\u001b[32m 2169\u001b[39m \u001b[38;5;28mself\u001b[39m._in_subscript_or_superscript = \u001b[38;5;28;01mFalse\u001b[39;00m\n",
|
||
"\u001b[31mValueError\u001b[39m: \nBTC/USDT Order Book — Spread: $0.01 (0.0000%) — Mid: $66,719.92\n ^\nParseException: Expected end of text, found '$' (at char 30), (line:1, col:31)"
|
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]
|
||
},
|
||
{
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"data": {
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"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"transient": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_real_orderbook(ob_df: pl.DataFrame, symbol: str):\n",
|
||
" \"\"\"Visualiza el order book real.\"\"\"\n",
|
||
" bids = ob_df.filter(pl.col('side') == 'bid').sort('price', descending=True)\n",
|
||
" asks = ob_df.filter(pl.col('side') == 'ask').sort('price')\n",
|
||
"\n",
|
||
" bid_prices = bids['price'].to_numpy()\n",
|
||
" bid_cum = np.cumsum(bids['qty'].to_numpy())\n",
|
||
" ask_prices = asks['price'].to_numpy()\n",
|
||
" ask_cum = np.cumsum(asks['qty'].to_numpy())\n",
|
||
"\n",
|
||
" fig, ax = plt.subplots(figsize=(12, 5))\n",
|
||
" ax.fill_between(bid_prices, bid_cum, step='post', color='#2ecc71', alpha=0.5, label='Bids')\n",
|
||
" ax.fill_between(ask_prices, ask_cum, step='pre', color='#e74c3c', alpha=0.5, label='Asks')\n",
|
||
" ax.set_xlabel('Precio (USDT)')\n",
|
||
" ax.set_ylabel('Cantidad acumulada (BTC)')\n",
|
||
"\n",
|
||
" best_bid = bid_prices[0]\n",
|
||
" best_ask = ask_prices[0]\n",
|
||
" spread = best_ask - best_bid\n",
|
||
" mid = (best_bid + best_ask) / 2\n",
|
||
"\n",
|
||
" ax.axvline(x=mid, color='gray', linestyle='--', linewidth=0.8)\n",
|
||
" ax.set_title(f'{symbol} Order Book — Spread: ${spread:.2f} ({spread/mid*100:.4f}%) — Mid: ${mid:,.2f}')\n",
|
||
" ax.legend()\n",
|
||
" ax.grid(True, alpha=0.3)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"plot_real_orderbook(ob_df, SYMBOL)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3. Trades recientes (fills)\n",
|
||
"\n",
|
||
"Esto es lo que ves en el tape público. Cada trade es un **fill** — no sabes si vienen de la misma orden."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Trades obtenidos: 1000\n",
|
||
"Rango: 2026-04-03T14:03:59.440Z → 2026-04-03T14:05:07.621Z\n",
|
||
"\n",
|
||
"Últimos 5 trades:\n",
|
||
"shape: (5, 6)\n",
|
||
"┌───────────────┬──────────────────────────┬──────────┬─────────┬──────┬────────────┐\n",
|
||
"│ timestamp ┆ datetime ┆ price ┆ qty ┆ side ┆ cost │\n",
|
||
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
|
||
"│ i64 ┆ str ┆ f64 ┆ f64 ┆ str ┆ f64 │\n",
|
||
"╞═══════════════╪══════════════════════════╪══════════╪═════════╪══════╪════════════╡\n",
|
||
"│ 1775225106686 ┆ 2026-04-03T14:05:06.686Z ┆ 66730.53 ┆ 0.00772 ┆ sell ┆ 515.159692 │\n",
|
||
"│ 1775225106686 ┆ 2026-04-03T14:05:06.686Z ┆ 66730.54 ┆ 0.0003 ┆ buy ┆ 20.019162 │\n",
|
||
"│ 1775225107139 ┆ 2026-04-03T14:05:07.139Z ┆ 66730.53 ┆ 0.10249 ┆ sell ┆ 6839.21202 │\n",
|
||
"│ 1775225107397 ┆ 2026-04-03T14:05:07.397Z ┆ 66730.54 ┆ 0.00233 ┆ buy ┆ 155.482158 │\n",
|
||
"│ 1775225107621 ┆ 2026-04-03T14:05:07.621Z ┆ 66730.54 ┆ 0.00009 ┆ buy ┆ 6.0057486 │\n",
|
||
"└───────────────┴──────────────────────────┴──────────┴─────────┴──────┴────────────┘\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def fetch_trades(symbol: str, limit: int = 1000) -> pl.DataFrame:\n",
|
||
" \"\"\"Obtiene trades recientes.\"\"\"\n",
|
||
" raw = exchange.fetch_trades(symbol, limit=limit)\n",
|
||
" records = [{\n",
|
||
" 'timestamp': t['timestamp'],\n",
|
||
" 'datetime': t['datetime'],\n",
|
||
" 'price': t['price'],\n",
|
||
" 'qty': t['amount'],\n",
|
||
" 'side': t['side'], # taker side\n",
|
||
" 'cost': t['cost'], # price * qty en quote currency\n",
|
||
" } for t in raw]\n",
|
||
" return pl.DataFrame(records)\n",
|
||
"\n",
|
||
"\n",
|
||
"trades = fetch_trades(SYMBOL, limit=1000)\n",
|
||
"print(f\"Trades obtenidos: {trades.shape[0]}\")\n",
|
||
"print(f\"Rango: {trades['datetime'].min()} → {trades['datetime'].max()}\")\n",
|
||
"print(f\"\\nÚltimos 5 trades:\")\n",
|
||
"print(trades.tail(5))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Buy trades: 475 (47.5%)\n",
|
||
"Sell trades: 525 (52.5%)\n",
|
||
"\n",
|
||
"Tamaño promedio: 0.017711 BTC\n",
|
||
"Tamaño mediano: 0.000440 BTC\n",
|
||
"Tamaño máximo: 2.754150 BTC\n",
|
||
"\n",
|
||
"Precio min: $66,710.66\n",
|
||
"Precio max: $66,765.71\n",
|
||
"Rango: $55.05\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Estadísticas básicas de los trades\n",
|
||
"buys = trades.filter(pl.col('side') == 'buy')\n",
|
||
"sells = trades.filter(pl.col('side') == 'sell')\n",
|
||
"\n",
|
||
"print(f\"Buy trades: {buys.shape[0]} ({buys.shape[0]/trades.shape[0]*100:.1f}%)\")\n",
|
||
"print(f\"Sell trades: {sells.shape[0]} ({sells.shape[0]/trades.shape[0]*100:.1f}%)\")\n",
|
||
"print(f\"\\nTamaño promedio: {trades['qty'].mean():.6f} BTC\")\n",
|
||
"print(f\"Tamaño mediano: {trades['qty'].median():.6f} BTC\")\n",
|
||
"print(f\"Tamaño máximo: {trades['qty'].max():.6f} BTC\")\n",
|
||
"print(f\"\\nPrecio min: ${trades['price'].min():,.2f}\")\n",
|
||
"print(f\"Precio max: ${trades['price'].max():,.2f}\")\n",
|
||
"print(f\"Rango: ${trades['price'].max() - trades['price'].min():,.2f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. Velas históricas (OHLCV)\n",
|
||
"\n",
|
||
"Las velas agregan trades en intervalos. Útiles para estimar σ en distintos timeframes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Velas 1m: 500\n",
|
||
"Rango: 2026-04-03 05:46:00 → 2026-04-03 14:05:00\n",
|
||
"shape: (3, 7)\n",
|
||
"┌───────────────┬──────────┬──────────┬──────────┬──────────┬─────────┬─────────────────────┐\n",
|
||
"│ timestamp ┆ open ┆ high ┆ low ┆ close ┆ volume ┆ datetime │\n",
|
||
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
|
||
"│ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ datetime[μs] │\n",
|
||
"╞═══════════════╪══════════╪══════════╪══════════╪══════════╪═════════╪═════════════════════╡\n",
|
||
"│ 1775224980000 ┆ 66661.31 ┆ 66748.03 ┆ 66655.0 ┆ 66736.76 ┆ 9.91011 ┆ 2026-04-03 14:03:00 │\n",
|
||
"│ 1775225040000 ┆ 66736.32 ┆ 66765.71 ┆ 66710.66 ┆ 66717.91 ┆ 16.2169 ┆ 2026-04-03 14:04:00 │\n",
|
||
"│ 1775225100000 ┆ 66717.91 ┆ 66730.54 ┆ 66717.91 ┆ 66722.04 ┆ 1.51069 ┆ 2026-04-03 14:05:00 │\n",
|
||
"└───────────────┴──────────┴──────────┴──────────┴──────────┴─────────┴─────────────────────┘\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def fetch_ohlcv(symbol: str, timeframe: str = '1m', limit: int = 500) -> pl.DataFrame:\n",
|
||
" \"\"\"Obtiene velas OHLCV.\"\"\"\n",
|
||
" raw = exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)\n",
|
||
" df = pl.DataFrame(raw, schema=['timestamp', 'open', 'high', 'low', 'close', 'volume'], orient='row')\n",
|
||
" df = df.with_columns(\n",
|
||
" (pl.col('timestamp').cast(pl.Int64) * 1000).cast(pl.Datetime('us')).alias('datetime')\n",
|
||
" )\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"# 1-minute candles, últimas 500\n",
|
||
"ohlcv_1m = fetch_ohlcv(SYMBOL, '1m', 500)\n",
|
||
"print(f\"Velas 1m: {ohlcv_1m.shape[0]}\")\n",
|
||
"print(f\"Rango: {ohlcv_1m['datetime'].min()} → {ohlcv_1m['datetime'].max()}\")\n",
|
||
"print(ohlcv_1m.tail(3))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1400x700 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"transient": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_candles_and_volume(ohlcv: pl.DataFrame, symbol: str, timeframe: str):\n",
|
||
" \"\"\"Gráfico de velas con volumen.\"\"\"\n",
|
||
" fig, axes = plt.subplots(2, 1, figsize=(14, 7), gridspec_kw={'height_ratios': [3, 1]}, sharex=True)\n",
|
||
"\n",
|
||
" dt = ohlcv['datetime'].to_numpy()\n",
|
||
" opens = ohlcv['open'].to_numpy()\n",
|
||
" closes = ohlcv['close'].to_numpy()\n",
|
||
" highs = ohlcv['high'].to_numpy()\n",
|
||
" lows = ohlcv['low'].to_numpy()\n",
|
||
" volumes = ohlcv['volume'].to_numpy()\n",
|
||
"\n",
|
||
" colors = ['#2ecc71' if c >= o else '#e74c3c' for o, c in zip(opens, closes)]\n",
|
||
"\n",
|
||
" # Velas\n",
|
||
" ax = axes[0]\n",
|
||
" for i in range(len(dt)):\n",
|
||
" ax.plot([i, i], [lows[i], highs[i]], color=colors[i], linewidth=0.5)\n",
|
||
" ax.plot([i, i], [opens[i], closes[i]], color=colors[i], linewidth=2)\n",
|
||
" ax.set_ylabel('Precio (USDT)')\n",
|
||
" ax.set_title(f'{symbol} — {timeframe} candles')\n",
|
||
" ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
" # Volumen\n",
|
||
" ax = axes[1]\n",
|
||
" ax.bar(range(len(dt)), volumes, color=colors, alpha=0.6, width=0.8)\n",
|
||
" ax.set_ylabel('Volumen (BTC)')\n",
|
||
" ax.set_xlabel('Vela')\n",
|
||
" ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"# Últimas 100 velas para que se vea claro\n",
|
||
"plot_candles_and_volume(ohlcv_1m.tail(100), SYMBOL, '1m')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 5. Estimación de parámetros sobre datos reales\n",
|
||
"\n",
|
||
"Aplicamos las mismas técnicas del notebook 03 pero sobre BTC/USDT real."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 5.1 Volatilidad (σ) desde velas"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"σ por minuto: 0.000400\n",
|
||
"σ por hora: 0.003101\n",
|
||
"σ por día: 0.0152 (1.52%)\n",
|
||
"σ anualizada: 0.2902 (29.0%)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Retornos logarítmicos close-to-close\n",
|
||
"closes = ohlcv_1m['close'].to_numpy()\n",
|
||
"log_returns = np.diff(np.log(closes))\n",
|
||
"\n",
|
||
"sigma_1m = np.std(log_returns)\n",
|
||
"sigma_1h = sigma_1m * np.sqrt(60) # escalar a 1 hora\n",
|
||
"sigma_1d = sigma_1m * np.sqrt(60 * 24) # escalar a 1 día\n",
|
||
"sigma_annual = sigma_1d * np.sqrt(365) # anualizada\n",
|
||
"\n",
|
||
"print(f\"σ por minuto: {sigma_1m:.6f}\")\n",
|
||
"print(f\"σ por hora: {sigma_1h:.6f}\")\n",
|
||
"print(f\"σ por día: {sigma_1d:.4f} ({sigma_1d*100:.2f}%)\")\n",
|
||
"print(f\"σ anualizada: {sigma_annual:.4f} ({sigma_annual*100:.1f}%)\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 5.2 Arrival rate (λ) de trades"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Tiempo medio entre trades: 189.4 ms\n",
|
||
"Tiempo mediano entre trades: 76.0 ms\n",
|
||
"λ (trades/segundo): 5.3\n",
|
||
"λ (trades/minuto): 317\n",
|
||
"\n",
|
||
"Recuerda: esto son FILLS, no órdenes originales\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Inter-arrival times entre trades consecutivos\n",
|
||
"timestamps = trades['timestamp'].to_numpy()\n",
|
||
"inter_arrivals_ms = np.diff(timestamps)\n",
|
||
"inter_arrivals_s = inter_arrivals_ms / 1000.0\n",
|
||
"\n",
|
||
"# Filtrar zeros (trades en el mismo milisegundo = probablemente mismo matching event)\n",
|
||
"inter_arrivals_s = inter_arrivals_s[inter_arrivals_s > 0]\n",
|
||
"\n",
|
||
"lambda_per_sec = 1.0 / np.mean(inter_arrivals_s)\n",
|
||
"lambda_per_min = lambda_per_sec * 60\n",
|
||
"\n",
|
||
"print(f\"Tiempo medio entre trades: {np.mean(inter_arrivals_s)*1000:.1f} ms\")\n",
|
||
"print(f\"Tiempo mediano entre trades: {np.median(inter_arrivals_s)*1000:.1f} ms\")\n",
|
||
"print(f\"λ (trades/segundo): {lambda_per_sec:.1f}\")\n",
|
||
"print(f\"λ (trades/minuto): {lambda_per_min:.0f}\")\n",
|
||
"print(f\"\\nRecuerda: esto son FILLS, no órdenes originales\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 5.3 Clustering (Hawkes) — ¿los trades generan más trades?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Var/Mean ratio: 34.56\n",
|
||
" → Hay clustering significativo (Hawkes). Los trades generan más trades.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Agrupar trades por segundo y calcular autocorrelación\n",
|
||
"trades_per_sec = trades.with_columns(\n",
|
||
" (pl.col('timestamp') // 1000).alias('second')\n",
|
||
").group_by('second').agg(pl.len().alias('n_trades')).sort('second')\n",
|
||
"\n",
|
||
"arrivals = trades_per_sec['n_trades'].to_numpy()\n",
|
||
"\n",
|
||
"# Autocorrelación\n",
|
||
"max_lag = 30\n",
|
||
"mean_arr = np.mean(arrivals)\n",
|
||
"var_arr = np.var(arrivals)\n",
|
||
"acf = np.array([\n",
|
||
" np.mean((arrivals[lag:] - mean_arr) * (arrivals[:-lag] - mean_arr)) / var_arr\n",
|
||
" if lag > 0 else 1.0\n",
|
||
" for lag in range(max_lag)\n",
|
||
"])\n",
|
||
"\n",
|
||
"# Var/Mean ratio (dispersion index)\n",
|
||
"dispersion = var_arr / mean_arr\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
|
||
"\n",
|
||
"axes[0].bar(range(max_lag), acf, color='#e67e22', alpha=0.6)\n",
|
||
"axes[0].axhline(y=0, color='black', linewidth=0.5)\n",
|
||
"axes[0].axhline(y=1.96/np.sqrt(len(arrivals)), color='blue', linestyle='--', linewidth=0.8, label='95% CI')\n",
|
||
"axes[0].axhline(y=-1.96/np.sqrt(len(arrivals)), color='blue', linestyle='--', linewidth=0.8)\n",
|
||
"axes[0].set_title('Autocorrelación de trades/segundo')\n",
|
||
"axes[0].set_xlabel('Lag (segundos)')\n",
|
||
"axes[0].legend(fontsize=8)\n",
|
||
"axes[0].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"axes[1].hist(arrivals, bins=50, color='#3498db', alpha=0.6, density=True)\n",
|
||
"axes[1].set_title(f'Distribución de trades/segundo\\nMedia={mean_arr:.1f}, Var/Mean={dispersion:.1f}')\n",
|
||
"axes[1].set_xlabel('Trades por segundo')\n",
|
||
"axes[1].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f\"Var/Mean ratio: {dispersion:.2f}\")\n",
|
||
"if dispersion > 1.5:\n",
|
||
" print(\" → Hay clustering significativo (Hawkes). Los trades generan más trades.\")\n",
|
||
"else:\n",
|
||
" print(\" → Cercano a Poisson. Poco clustering.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 5.4 Distribución de tamaños — ¿hay ballenas?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Tamaño mediano: 0.000440 BTC ($29.37)\n",
|
||
"Tamaño p99: 0.380318 BTC ($25,389.70)\n",
|
||
"Tamaño max: 2.754150 BTC ($183,864.85)\n",
|
||
"Pareto α (cola): 0.76\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"sizes = trades['qty'].to_numpy()\n",
|
||
"sizes = sizes[sizes > 0]\n",
|
||
"\n",
|
||
"# Estimar exponente Pareto (MLE)\n",
|
||
"x_min = np.percentile(sizes, 90) # usar percentil 90 como x_min (zona de cola)\n",
|
||
"tail = sizes[sizes >= x_min]\n",
|
||
"alpha_est = len(tail) / np.sum(np.log(tail / x_min))\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
|
||
"\n",
|
||
"# Histograma\n",
|
||
"axes[0].hist(sizes, bins=100, color='#2ecc71', alpha=0.6, density=True)\n",
|
||
"axes[0].set_title('Distribución de tamaños de trades')\n",
|
||
"axes[0].set_xlabel('Tamaño (BTC)')\n",
|
||
"axes[0].set_yscale('log')\n",
|
||
"axes[0].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# CCDF log-log (survival function)\n",
|
||
"sizes_sorted = np.sort(sizes)[::-1]\n",
|
||
"ranks = np.arange(1, len(sizes_sorted) + 1) / len(sizes_sorted)\n",
|
||
"axes[1].loglog(sizes_sorted, ranks, '.', markersize=1, alpha=0.4, color='#2ecc71')\n",
|
||
"# Fit Pareto\n",
|
||
"x_fit = np.logspace(np.log10(x_min), np.log10(sizes.max()), 50)\n",
|
||
"axes[1].loglog(x_fit, (x_fit / x_min) ** (-alpha_est) * (len(tail)/len(sizes)),\n",
|
||
" 'r-', linewidth=2, label=f'Pareto α={alpha_est:.2f}')\n",
|
||
"axes[1].set_title('CCDF (complementary CDF) — cola pesada')\n",
|
||
"axes[1].set_xlabel('Tamaño (BTC)')\n",
|
||
"axes[1].set_ylabel('P(X > x)')\n",
|
||
"axes[1].legend()\n",
|
||
"axes[1].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f\"Tamaño mediano: {np.median(sizes):.6f} BTC (${np.median(sizes) * ticker['last']:,.2f})\")\n",
|
||
"print(f\"Tamaño p99: {np.percentile(sizes, 99):.6f} BTC (${np.percentile(sizes, 99) * ticker['last']:,.2f})\")\n",
|
||
"print(f\"Tamaño max: {sizes.max():.6f} BTC (${sizes.max() * ticker['last']:,.2f})\")\n",
|
||
"print(f\"Pareto α (cola): {alpha_est:.2f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 5.5 Detección de jumps en retornos reales"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Jumps detectados: 7 de 499 velas (1.4%)\n",
|
||
"Kurtosis: 4.7 (Normal=3, >3 = colas pesadas)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Retornos de 1 minuto\n",
|
||
"threshold = 3 * sigma_1m\n",
|
||
"jump_mask = np.abs(log_returns) > threshold\n",
|
||
"n_jumps = np.sum(jump_mask)\n",
|
||
"jump_intensity = n_jumps / len(log_returns)\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
|
||
"\n",
|
||
"# Retornos con jumps marcados\n",
|
||
"ax = axes[0]\n",
|
||
"ax.plot(log_returns, linewidth=0.5, color='#3498db', alpha=0.6)\n",
|
||
"jump_indices = np.where(jump_mask)[0]\n",
|
||
"ax.scatter(jump_indices, log_returns[jump_indices], color='red', s=20, zorder=5, label=f'Jumps ({n_jumps})')\n",
|
||
"ax.axhline(y=threshold, color='red', linestyle='--', linewidth=0.8, alpha=0.5)\n",
|
||
"ax.axhline(y=-threshold, color='red', linestyle='--', linewidth=0.8, alpha=0.5)\n",
|
||
"ax.set_title('Retornos 1m con jumps detectados (> 3σ)')\n",
|
||
"ax.set_ylabel('Log-return')\n",
|
||
"ax.legend(fontsize=8)\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# QQ plot\n",
|
||
"from scipy.stats import probplot\n",
|
||
"probplot(log_returns, dist=\"norm\", plot=axes[1])\n",
|
||
"axes[1].set_title('QQ-Plot: retornos vs Normal\\n(colas pesadas = desviación en extremos)')\n",
|
||
"axes[1].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f\"Jumps detectados: {n_jumps} de {len(log_returns)} velas ({jump_intensity*100:.1f}%)\")\n",
|
||
"print(f\"Kurtosis: {float(np.mean((log_returns - np.mean(log_returns))**4) / np.std(log_returns)**4):.1f} (Normal=3, >3 = colas pesadas)\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 6. Resumen: perfil del mercado BTC/USDT"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"============================================================\n",
|
||
" PERFIL DE MERCADO: BTC/USDT\n",
|
||
" 2026-04-03 16:07:14\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
" Precio: $66,759.20\n",
|
||
" Spread: $0.01 (0.0000%)\n",
|
||
" Vol 24h: 15,644 BTC\n",
|
||
"\n",
|
||
" σ (1 min): 0.000400\n",
|
||
" σ (diaria): 0.0152 (1.52%)\n",
|
||
" σ (anual): 0.29 (29%)\n",
|
||
"\n",
|
||
" λ (fills/seg): 5.3\n",
|
||
" Clustering: Var/Mean = 34.6 (Hawkes)\n",
|
||
"\n",
|
||
" Tamaño mediano: 0.000440 BTC\n",
|
||
" Pareto α: 0.76\n",
|
||
" Kurtosis: 4.7\n",
|
||
" Jumps (>3σ): 1.4%\n",
|
||
"\n",
|
||
"============================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"best_bid = ob_df.filter(pl.col('side') == 'bid')['price'].max()\n",
|
||
"best_ask = ob_df.filter(pl.col('side') == 'ask')['price'].min()\n",
|
||
"spread = best_ask - best_bid\n",
|
||
"\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(f\" PERFIL DE MERCADO: {SYMBOL}\")\n",
|
||
"print(f\" {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(f\"\")\n",
|
||
"print(f\" Precio: ${ticker['last']:,.2f}\")\n",
|
||
"print(f\" Spread: ${spread:.2f} ({spread/ticker['last']*100:.4f}%)\")\n",
|
||
"print(f\" Vol 24h: {ticker['baseVolume']:,.0f} BTC\")\n",
|
||
"print(f\"\")\n",
|
||
"print(f\" σ (1 min): {sigma_1m:.6f}\")\n",
|
||
"print(f\" σ (diaria): {sigma_1d:.4f} ({sigma_1d*100:.2f}%)\")\n",
|
||
"print(f\" σ (anual): {sigma_annual:.2f} ({sigma_annual*100:.0f}%)\")\n",
|
||
"print(f\"\")\n",
|
||
"print(f\" λ (fills/seg): {lambda_per_sec:.1f}\")\n",
|
||
"print(f\" Clustering: Var/Mean = {dispersion:.1f} {'(Hawkes)' if dispersion > 1.5 else '(~Poisson)'}\")\n",
|
||
"print(f\"\")\n",
|
||
"print(f\" Tamaño mediano: {np.median(sizes):.6f} BTC\")\n",
|
||
"print(f\" Pareto α: {alpha_est:.2f}\")\n",
|
||
"print(f\" Kurtosis: {float(np.mean((log_returns - np.mean(log_returns))**4) / np.std(log_returns)**4):.1f}\")\n",
|
||
"print(f\" Jumps (>3σ): {jump_intensity*100:.1f}%\")\n",
|
||
"print(f\"\")\n",
|
||
"print(\"=\" * 60)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 7. Guardar datos para análisis offline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Guardados en data/:\n",
|
||
" binance_btcusdt_trades.csv (1000 trades)\n",
|
||
" binance_btcusdt_ohlcv_1m.csv (500 velas)\n",
|
||
" binance_btcusdt_orderbook.csv (40 niveles)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Guardar todo en data/\n",
|
||
"trades.write_csv('../data/binance_btcusdt_trades.csv')\n",
|
||
"ohlcv_1m.write_csv('../data/binance_btcusdt_ohlcv_1m.csv')\n",
|
||
"ob_df.write_csv('../data/binance_btcusdt_orderbook.csv')\n",
|
||
"\n",
|
||
"print(f\"Guardados en data/:\")\n",
|
||
"print(f\" binance_btcusdt_trades.csv ({trades.shape[0]} trades)\")\n",
|
||
"print(f\" binance_btcusdt_ohlcv_1m.csv ({ohlcv_1m.shape[0]} velas)\")\n",
|
||
"print(f\" binance_btcusdt_orderbook.csv ({ob_df.shape[0]} niveles)\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Top 5 bids:\n",
|
||
"shape: (5, 3)\n",
|
||
"┌──────────┬─────────┬──────┐\n",
|
||
"│ price ┆ qty ┆ side │\n",
|
||
"│ --- ┆ --- ┆ --- │\n",
|
||
"│ f64 ┆ f64 ┆ str │\n",
|
||
"╞══════════╪═════════╪══════╡\n",
|
||
"│ 66793.89 ┆ 0.08639 ┆ bid │\n",
|
||
"│ 66793.88 ┆ 0.00072 ┆ bid │\n",
|
||
"│ 66793.87 ┆ 0.00008 ┆ bid │\n",
|
||
"│ 66793.86 ┆ 0.00024 ┆ bid │\n",
|
||
"│ 66793.85 ┆ 0.05024 ┆ bid │\n",
|
||
"└──────────┴─────────┴──────┘\n",
|
||
"\n",
|
||
"Top 5 asks:\n",
|
||
"shape: (5, 3)\n",
|
||
"┌──────────┬─────────┬──────┐\n",
|
||
"│ price ┆ qty ┆ side │\n",
|
||
"│ --- ┆ --- ┆ --- │\n",
|
||
"│ f64 ┆ f64 ┆ str │\n",
|
||
"╞══════════╪═════════╪══════╡\n",
|
||
"│ 66793.9 ┆ 1.94694 ┆ ask │\n",
|
||
"│ 66793.91 ┆ 0.00016 ┆ ask │\n",
|
||
"│ 66794.0 ┆ 0.00129 ┆ ask │\n",
|
||
"│ 66794.6 ┆ 0.00532 ┆ ask │\n",
|
||
"│ 66794.67 ┆ 0.00008 ┆ ask │\n",
|
||
"└──────────┴─────────┴──────┘\n",
|
||
"\n",
|
||
"Best bid: 66793.89 Best ask: 66793.9 Spread: 0.01 Mid: 66793.89\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# Fix: re-fetch orderbook con manejo de timestamp None\n",
|
||
"ob = exchange.fetch_order_book(SYMBOL, limit=20)\n",
|
||
"bids = pl.DataFrame(ob['bids'], schema=['price', 'qty'], orient='row').with_columns(pl.lit('bid').alias('side'))\n",
|
||
"asks = pl.DataFrame(ob['asks'], schema=['price', 'qty'], orient='row').with_columns(pl.lit('ask').alias('side'))\n",
|
||
"ob_df = pl.concat([bids, asks])\n",
|
||
"\n",
|
||
"print('Top 5 bids:')\n",
|
||
"print(ob_df.filter(pl.col('side') == 'bid').head(5))\n",
|
||
"print('\\nTop 5 asks:')\n",
|
||
"print(ob_df.filter(pl.col('side') == 'ask').head(5))\n",
|
||
"\n",
|
||
"best_bid = ob_df.filter(pl.col('side') == 'bid')['price'].max()\n",
|
||
"best_ask = ob_df.filter(pl.col('side') == 'ask')['price'].min()\n",
|
||
"spread = best_ask - best_bid\n",
|
||
"mid = (best_bid + best_ask) / 2\n",
|
||
"print(f'\\nBest bid: {best_bid} Best ask: {best_ask} Spread: {spread:.2f} Mid: {mid:.2f}')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# Depth chart del orderbook real\n",
|
||
"bids_plot = ob_df.filter(pl.col('side') == 'bid').sort('price', descending=True)\n",
|
||
"asks_plot = ob_df.filter(pl.col('side') == 'ask').sort('price')\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(12, 5))\n",
|
||
"ax.fill_between(bids_plot['price'].to_numpy(), np.cumsum(bids_plot['qty'].to_numpy()), step='post', color='#2ecc71', alpha=0.5, label='Bids')\n",
|
||
"ax.fill_between(asks_plot['price'].to_numpy(), np.cumsum(asks_plot['qty'].to_numpy()), step='pre', color='#e74c3c', alpha=0.5, label='Asks')\n",
|
||
"ax.axvline(x=mid, color='gray', linestyle='--', linewidth=0.8)\n",
|
||
"ax.set_xlabel('Precio (USDT)')\n",
|
||
"ax.set_ylabel('Cantidad acumulada (BTC)')\n",
|
||
"ax.set_title(f'{SYMBOL} Order Book — Spread: ${spread:.2f} — Mid: ${mid:,.2f}')\n",
|
||
"ax.legend()\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"σ por minuto: 0.000400\n",
|
||
"σ por hora: 0.003101\n",
|
||
"σ por día: 0.0152 (1.52%)\n",
|
||
"σ anualizada: 0.2902 (29.0%)\n",
|
||
"\n",
|
||
"Tiempo medio entre trades: 189.4 ms\n",
|
||
"Tiempo mediano entre trades: 76.0 ms\n",
|
||
"λ (fills/segundo): 5.3\n",
|
||
"λ (fills/minuto): 317\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# === ESTIMACIONES SOBRE DATOS REALES ===\n",
|
||
"\n",
|
||
"# 5.1 Volatilidad\n",
|
||
"closes = ohlcv_1m['close'].to_numpy()\n",
|
||
"log_returns = np.diff(np.log(closes))\n",
|
||
"sigma_1m = np.std(log_returns)\n",
|
||
"sigma_1h = sigma_1m * np.sqrt(60)\n",
|
||
"sigma_1d = sigma_1m * np.sqrt(60 * 24)\n",
|
||
"sigma_annual = sigma_1d * np.sqrt(365)\n",
|
||
"print(f'σ por minuto: {sigma_1m:.6f}')\n",
|
||
"print(f'σ por hora: {sigma_1h:.6f}')\n",
|
||
"print(f'σ por día: {sigma_1d:.4f} ({sigma_1d*100:.2f}%)')\n",
|
||
"print(f'σ anualizada: {sigma_annual:.4f} ({sigma_annual*100:.1f}%)')\n",
|
||
"\n",
|
||
"# 5.2 Arrival rate\n",
|
||
"timestamps_arr = trades['timestamp'].to_numpy()\n",
|
||
"inter_arrivals_ms = np.diff(timestamps_arr)\n",
|
||
"inter_arrivals_s = inter_arrivals_ms / 1000.0\n",
|
||
"inter_arrivals_s = inter_arrivals_s[inter_arrivals_s > 0]\n",
|
||
"lambda_per_sec = 1.0 / np.mean(inter_arrivals_s)\n",
|
||
"lambda_per_min = lambda_per_sec * 60\n",
|
||
"print(f'\\nTiempo medio entre trades: {np.mean(inter_arrivals_s)*1000:.1f} ms')\n",
|
||
"print(f'Tiempo mediano entre trades: {np.median(inter_arrivals_s)*1000:.1f} ms')\n",
|
||
"print(f'λ (fills/segundo): {lambda_per_sec:.1f}')\n",
|
||
"print(f'λ (fills/minuto): {lambda_per_min:.0f}')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Var/Mean ratio: 34.56\n",
|
||
" → Hay clustering significativo (Hawkes)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# 5.3 Clustering (Hawkes)\n",
|
||
"trades_per_sec = trades.with_columns(\n",
|
||
" (pl.col('timestamp') // 1000).alias('second')\n",
|
||
").group_by('second').agg(pl.len().alias('n_trades')).sort('second')\n",
|
||
"arrivals = trades_per_sec['n_trades'].to_numpy()\n",
|
||
"\n",
|
||
"max_lag = 30\n",
|
||
"mean_arr = np.mean(arrivals)\n",
|
||
"var_arr = np.var(arrivals)\n",
|
||
"acf = np.array([\n",
|
||
" np.mean((arrivals[lag:] - mean_arr) * (arrivals[:-lag] - mean_arr)) / var_arr\n",
|
||
" if lag > 0 else 1.0\n",
|
||
" for lag in range(max_lag)\n",
|
||
"])\n",
|
||
"dispersion = var_arr / mean_arr\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
|
||
"axes[0].bar(range(max_lag), acf, color='#e67e22', alpha=0.6)\n",
|
||
"axes[0].axhline(y=0, color='black', linewidth=0.5)\n",
|
||
"axes[0].axhline(y=1.96/np.sqrt(len(arrivals)), color='blue', linestyle='--', linewidth=0.8, label='95% CI')\n",
|
||
"axes[0].axhline(y=-1.96/np.sqrt(len(arrivals)), color='blue', linestyle='--', linewidth=0.8)\n",
|
||
"axes[0].set_title('Autocorrelación de trades/segundo')\n",
|
||
"axes[0].set_xlabel('Lag (segundos)')\n",
|
||
"axes[0].legend(fontsize=8)\n",
|
||
"axes[0].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"axes[1].hist(arrivals, bins=30, color='#3498db', alpha=0.6, density=True)\n",
|
||
"axes[1].set_title(f'Distribución de trades/segundo\\nMedia={mean_arr:.1f}, Var/Mean={dispersion:.1f}')\n",
|
||
"axes[1].set_xlabel('Trades por segundo')\n",
|
||
"axes[1].grid(True, alpha=0.3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f'Var/Mean ratio: {dispersion:.2f}')\n",
|
||
"if dispersion > 1.5:\n",
|
||
" print(' → Hay clustering significativo (Hawkes)')\n",
|
||
"else:\n",
|
||
" print(' → Cercano a Poisson')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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TpkzRV6ikBV3klx4M6uRaoVDgxIkTKC0tRXV1NX788UcEBQVJFXKbwEnbFpiYmMDb2xs5OTliW0NDA3JycjB48OAmzxk8eLBKfwDIzs5utj9pR5vcNFZfX4/jx4/D1tZWX2GSmnjfPFiUSiXvGz0QBAFz5sxBeno6cnNz0bt371bP4b1jGNrkpjE+c4i0p822WPTg0EV+i4uL4e/vD7lcDn9/f0RERMDDw0Mf4ZKGdJHfAwcOIDU1Fdu2bYOXlxe8vLxw/PhxfYRLGtLVn8+BgYEIDQ3FDz/8AHt7e074GiF1ct2xY0esXLkSw4YNg5eXF+bNm4euXbtKEW6b0VHqAIzd3LlzMXXqVPj4+EChUCAxMRE1NTV48cUXAQBhYWGws7MTixpERkbiqaeewsqVKzFq1Ch89913OHLkCPfd0QNNc7N06VIMGjQIffv2RVVVFeLj41FcXIwZM2ZIeRltUnV1tcpqsvPnz0OpVMLa2hq9evXCG2+8gdLSUiQnJwMAXn75ZaxevRoLFizAtGnTkJubi7S0NGRlZUl1CW2WprlJTExE79690a9fP9TW1mLdunXIzc3Frl27pLqENmv27NlISUlBRkYGOnfuLP7lx9LSUlxxzmeONLTJDZ85RMYrPDxc6hBIxxQKBZRKpdRhkJ48+eSTaGhokDoM0qPdu3dLHQLpyHPPPYfnnntO6jDaDE7atmL8+PG4fPkyYmNjUV5eDi8vL+zYsUP814WLFy+iQ4f/L1j28/NDSkoKFi1ahDfffBMuLi7Ytm1bi8VKSDua5ubKlSuYOXMmysvL0aVLF3h7e6OgoADu7u5SXUKbdeTIEQwbNkz8PHfuXADA1KlTsWHDBpSVleHixYvi8d69eyMrKwuvv/46Pv74Y9jb22PdunX8rxR6oGlu6urqMG/ePJSWlsLMzAyenp7YvXu3yhikG2vXrgUABAQEqLSvX79enGDgM0ca2uSGzxwi3dHFtlhkvJjfto35bduY3/aDuZaGTBAEQeogiIiIiIiIAEAmkyE9PR0hISFim6+vLxQKBVatWgXg9rZYvXr1wpw5c7Bw4UKJIiVtML9tG/PbtjG/7QdzbRy40paIiIiIiCTV2vY9rW2LRcaN+W3bmN+2jfltP5hrIyQQERERERFJKC8vTwBwz2vq1Klin1WrVgm9evUSTExMBIVCIRw6dEi6gEkjzG/bxvy2bcxv+8FcGx9uj0BERERERERERERkRDq03oWIiIiIiIiIiIiIDIWTtkRERERERERERERGhJO2REREREREREREREaEk7ZERERERERERERERoSTtkRERERERERERERGhJO2RERGYseOHejSpQuio6Oxd+9eTJ06VSfjfvnll3j66ad1MpamkpKSMHr0aEm+m4iIiIiIiOhBxUlbImpXZDJZi68lS5ZIFlt6ejq++OIL3LhxA+Hh4Zg+ffp9j1lbW4u3334bixcvFtuWLFmics2Wlpbw9/dHfn4+AGDPnj2t/k579uwBAGzduhUBAQGwtLSEubk5PD09sXTpUlRWVgIApk2bhsLCQuzbt+++r4WIiIiIiIiovZAJgiBIHQQRkaGUl5eL71NTUxEbG4vTp0+Lbebm5jA3N5ciNL3YuHEjli9fjl9//VVsW7JkCbZs2YLdu3cDACorK5GQkIDNmzfj0qVLMDU1FSddASAyMhLXrl3D+vXrxTZra2u88847eP/99/H6669jzJgx6NmzJ86ePYukpCQMHToUkZGRAID58+fjwoUL2Lx5s4GumoiIiIiIiOjBxpW2RNSu2NjYiC9LS0vIZDLxc01NDSZNmoQePXrA3NwcAwcOFCc273JycsLy5csRFhYGc3NzODo6IjMzE5cvX0ZwcLC42vTIkSPiOX///TcmTJgAOzs7mJmZwcPDA5s2bVIZNyAgAK+99hoWLFgAa2tr2NjY3LPq9+LFi+J3WFhYYNy4caioqGjxer/77rsmtyfo2LGjeN3u7u5YunQpqqurcebMGZiYmKj8Tqampnj44YdV2pRKJeLi4rBy5UrEx8fDz88PTk5OGDFiBLZu3aqytcPo0aORmZmJGzduqJsmIiIiIiIionaNk7ZERHdUV1fjmWeeQU5ODo4dO4aRI0di9OjRuHjxokq/jz76CEOGDMGxY8cwatQoTJkyBWFhYZg8eTIKCwvh7OyMsLAw3P2PDLW1tfD29kZWVhZOnDiBl156CVOmTMFPP/2kMu7XX3+NRx99FIcPH8YHH3yApUuXIjs7GwDQ0NCA4OBgVFZWIj8/H9nZ2Th37hzGjx/f4jXt378fPj4+Lfb5999/sX79elhZWcHNzU2t3+rbb7+Fubk5Xn311SaPW1lZie99fHxw69YtHD58WK2xiYiIiIiIiNo7TtoSEd0hl8sxa9Ys9O/fHy4uLli2bBmcnZ2RmZmp0u+ZZ57BrFmz4OLigtjYWFy7dg0DBw5EaGgoXF1dERMTg6KiInEVrJ2dHaKjo+Hl5YU+ffogIiICI0eORFpamsq4np6eWLx4MVxcXBAWFgYfHx/k5OQAAHJycnD8+HGkpKTA29sbvr6+SE5ORn5+Pn7++ecmr6eqqgpXr15Fz5497zl2/PhxcSsIU1NTJCQkYNOmTbCwsFDrtzp79iz69OmDTp06tdrXzMwMlpaWKC4uVmtsIiIiImrbWICXiKh1nLQlIrqjuroa0dHRePzxx2FlZQVzc3MUFRXds9LW09NTfN+jRw8AgIeHxz1tf/75JwCgvr4ey5Ytg4eHB6ytrWFubo6dO3e2OC4A2NraimMUFRXBwcEBDg4O4nF3d3dYWVmhqKioyeu5ux3BI488cs8xNzc3KJVKKJVKHD16FK+88gpCQ0NVtnVoiabboZuamuL69esanUNERERE2mMBXhbgJaIHW0epAyAiMhbR0dHIzs5GQkIC+vbtC1NTU4wdOxZ1dXUq/f67ulQmkzXb1tDQAACIj4/Hxx9/jMTERHh4eODRRx9FVFRUi+PeHefuGNro2rUrZDIZrly5cs8xExMT9O3bV/w8YMAAbNu2DYmJidi4cWOrY7u6umL//v24efOmWqttKysr8dhjj2l2AURERESktbKyMvF9cwV4pfLZZ58BAMaOHauzMbds2QILCwsMGTJEpb1fv373FOB99tlncenSJfj5+an8Ts0V4H3rrbfEArxxcXEqBXi/+eYbREZGwsTEBBMnTsQnn3wCf39/nV0XEbVfXGlLRHTHgQMHEB4ejjFjxsDDwwM2Nja4cOGCTsYNDg7G5MmTIZfL0adPH5w5c0ajMR5//HGUlJSgpKREbDt16hSqqqrg7u7e5DkmJiZwd3fHqVOn1PqOhx56SO1iYRMnTkR1dTU+/fTTJo9XVVWJ73///XfU1tZiwIABao1NRERERPePBXhvYwFeInpQcdKWiOgOFxcXfP/991Aqlfjll18wceLE+1rp+t9xs7OzUVBQgKKiIsyaNavVv3Q2FhgYCA8PD0yaNAmFhYX46aefEBYWhqeeeqrFQmNBQUHYv3//Pe23bt1CeXk5ysvLcfbsWSxfvhynTp1CcHCwWvH4+vpiwYIFmDdvHhYsWICDBw+iuLgYOTk5CA0Nxddffy323bdvH/r06QNnZ2eNrpmIiIiI9IMFeFmAl4iMH7dHICK648MPP8S0adPg5+eHbt26ISYmBteuXbvvcRctWoRz584hKCgIZmZmeOmllxASEoKrV6+qPYZMJkNGRgYiIiIwdOhQdOjQASNHjsSqVataPG/69Onw8fHB1atXYWlpKbafPHkStra2AG4XCnN2dsbatWsRFhamdkzvv/8+vL29sWbNGiQlJaGhoQHOzs4YO3asyoqDTZs2YebMmWqPS0RERET6JZfLIZfLxc/Lli1Deno6MjMzMWfOHLH9bgFeAIiNjcXatWvFArwAEBMTg8GDB6OiogI2NjZiAd67IiIisHPnTqSlpUGhUIjtdwvwArcXOKxevRo5OTkYMWKEWID3/PnzYj2H5ORk9OvXDz///DMGDhx4z/WoU4AXAK5fv47OnTsjNTWVBXiJyOhx0paI2q3w8HCEh4eLn52cnJCbm6vSZ/bs2Sqfm9ouoXFRLicnJ5U2a2trbNu2rcVY7hY4+K/G5/Tq1QsZGRktjtOYu7s7Ro0ahU8//RRvvPEGgNsFGTQpPLFhw4Zmj40bNw7jxo1r9vjJkyehVCqRlpam9vcRERERkX5VV1djyZIlyMrKQllZGW7duoUbN27cVwFeGxsb1NfXIy4uDmlpaSgtLUVdXR3+/fdfmJmZNTsuoFkB3qYmbVsrwJuZmQkA+Oeff5CamorQ0FDk5eW1ujIXYAFeIpIOt0cgImrj4uPjJSs0UVZWhuTkZJVVvkREREQkrejoaKSnpyMuLg779u2DUqmEh4eHzgrwxsTEIC8vD0qlEkFBQUZRgLdv374YMGAA3nvvPdjZ2SExMVGtsV1dXXHu3DncvHlTrf4swEtEusJJWyKiNs7JyQkRERGSfHdgYCCCgoIk+W4iIiIiahoL8LIALxEZP07aEhEREREREbUjLMDLArxEZPy4py0RERERERFRO8ICvCzAS0TGTyZouqs2EREREREREZGRCQ0NxRNPPCEW4DWkkydPYvjw4Thz5gzrORCRTnB7BCIiIiIiIiJ64LEALxG1JVxpS0RERERERERERGREuNKWiIiIiIiIiIiIyIhw0paIiIiIiIiIiIjIiHDSloiIiIiIiIiIiMiIcNKWiIiIiIiIiIiIyIhw0paIiIiIiIiIiIjIiHDSloiIiIiIiIiIiMiIcNKWiIiIiIiIiIiIyIhw0paIiIiIiIiIiIjIiHDSloiIiIiIiIiIiMiI/A+7LVpr6z2RlgAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Tamaño mediano: 0.000440 BTC ($29.37)\n",
|
||
"Tamaño p99: 0.380318 BTC ($25,389.70)\n",
|
||
"Tamaño max: 2.754150 BTC ($183,864.85)\n",
|
||
"Pareto α: 0.76\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# 5.4 Distribución de tamaños\n",
|
||
"sizes = trades['qty'].to_numpy()\n",
|
||
"sizes = sizes[sizes > 0]\n",
|
||
"x_min = np.percentile(sizes, 90)\n",
|
||
"tail = sizes[sizes >= x_min]\n",
|
||
"alpha_est = len(tail) / np.sum(np.log(tail / x_min))\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
|
||
"axes[0].hist(sizes, bins=100, color='#2ecc71', alpha=0.6, density=True)\n",
|
||
"axes[0].set_title('Distribución de tamaños de trades')\n",
|
||
"axes[0].set_xlabel('Tamaño (BTC)')\n",
|
||
"axes[0].set_yscale('log')\n",
|
||
"axes[0].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"sizes_sorted = np.sort(sizes)[::-1]\n",
|
||
"ranks = np.arange(1, len(sizes_sorted) + 1) / len(sizes_sorted)\n",
|
||
"axes[1].loglog(sizes_sorted, ranks, '.', markersize=2, alpha=0.4, color='#2ecc71')\n",
|
||
"x_fit = np.logspace(np.log10(x_min), np.log10(sizes.max()), 50)\n",
|
||
"axes[1].loglog(x_fit, (x_fit / x_min) ** (-alpha_est) * (len(tail)/len(sizes)), 'r-', linewidth=2, label=f'Pareto α={alpha_est:.2f}')\n",
|
||
"axes[1].set_title('CCDF — cola pesada')\n",
|
||
"axes[1].set_xlabel('Tamaño (BTC)')\n",
|
||
"axes[1].set_ylabel('P(X > x)')\n",
|
||
"axes[1].legend()\n",
|
||
"axes[1].grid(True, alpha=0.3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f'Tamaño mediano: {np.median(sizes):.6f} BTC (${np.median(sizes) * ticker[\"last\"]:,.2f})')\n",
|
||
"print(f'Tamaño p99: {np.percentile(sizes, 99):.6f} BTC (${np.percentile(sizes, 99) * ticker[\"last\"]:,.2f})')\n",
|
||
"print(f'Tamaño max: {sizes.max():.6f} BTC (${sizes.max() * ticker[\"last\"]:,.2f})')\n",
|
||
"print(f'Pareto α: {alpha_est:.2f}')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Jumps: 7/499 (1.4%)\n",
|
||
"Kurtosis: 4.7 (Normal=3, >3 = colas pesadas)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# 5.5 Jumps y QQ-plot\n",
|
||
"from scipy.stats import probplot\n",
|
||
"threshold = 3 * sigma_1m\n",
|
||
"jump_mask = np.abs(log_returns) > threshold\n",
|
||
"n_jumps = np.sum(jump_mask)\n",
|
||
"jump_intensity = n_jumps / len(log_returns)\n",
|
||
"kurtosis = float(np.mean((log_returns - np.mean(log_returns))**4) / np.std(log_returns)**4)\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
|
||
"ax = axes[0]\n",
|
||
"ax.plot(log_returns, linewidth=0.5, color='#3498db', alpha=0.6)\n",
|
||
"jump_indices = np.where(jump_mask)[0]\n",
|
||
"ax.scatter(jump_indices, log_returns[jump_indices], color='red', s=20, zorder=5, label=f'Jumps ({n_jumps})')\n",
|
||
"ax.axhline(y=threshold, color='red', linestyle='--', linewidth=0.8, alpha=0.5)\n",
|
||
"ax.axhline(y=-threshold, color='red', linestyle='--', linewidth=0.8, alpha=0.5)\n",
|
||
"ax.set_title('Retornos 1m con jumps (> 3σ)')\n",
|
||
"ax.legend(fontsize=8)\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"probplot(log_returns, dist='norm', plot=axes[1])\n",
|
||
"axes[1].set_title('QQ-Plot: retornos vs Normal')\n",
|
||
"axes[1].grid(True, alpha=0.3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(f'Jumps: {n_jumps}/{len(log_returns)} ({jump_intensity*100:.1f}%)')\n",
|
||
"print(f'Kurtosis: {kurtosis:.1f} (Normal=3, >3 = colas pesadas)')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"============================================================\n",
|
||
" PERFIL DE MERCADO: BTC/USDT\n",
|
||
"============================================================\n",
|
||
" Precio: $66,759.20\n",
|
||
" Spread: $0.01 (0.0000%)\n",
|
||
" Vol 24h: 15,644 BTC\n",
|
||
"\n",
|
||
" σ (1 min): 0.000400\n",
|
||
" σ (diaria): 0.0152 (1.52%)\n",
|
||
" σ (anual): 0.29 (29%)\n",
|
||
"\n",
|
||
" λ (fills/seg): 5.3\n",
|
||
" Clustering: Var/Mean = 34.6 (Hawkes)\n",
|
||
"\n",
|
||
" Tamaño mediano: 0.000440 BTC\n",
|
||
" Pareto α: 0.76\n",
|
||
" Kurtosis: 4.7\n",
|
||
" Jumps (>3σ): 1.4%\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"Datos guardados en data/\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# === RESUMEN: PERFIL DE MERCADO BTC/USDT ===\n",
|
||
"print('=' * 60)\n",
|
||
"print(f' PERFIL DE MERCADO: {SYMBOL}')\n",
|
||
"print('=' * 60)\n",
|
||
"print(f' Precio: ${ticker[\"last\"]:,.2f}')\n",
|
||
"print(f' Spread: ${spread:.2f} ({spread/ticker[\"last\"]*100:.4f}%)')\n",
|
||
"print(f' Vol 24h: {ticker[\"baseVolume\"]:,.0f} BTC')\n",
|
||
"print()\n",
|
||
"print(f' σ (1 min): {sigma_1m:.6f}')\n",
|
||
"print(f' σ (diaria): {sigma_1d:.4f} ({sigma_1d*100:.2f}%)')\n",
|
||
"print(f' σ (anual): {sigma_annual:.2f} ({sigma_annual*100:.0f}%)')\n",
|
||
"print()\n",
|
||
"print(f' λ (fills/seg): {lambda_per_sec:.1f}')\n",
|
||
"print(f' Clustering: Var/Mean = {dispersion:.1f} (Hawkes)')\n",
|
||
"print()\n",
|
||
"print(f' Tamaño mediano: {np.median(sizes):.6f} BTC')\n",
|
||
"print(f' Pareto α: {alpha_est:.2f}')\n",
|
||
"print(f' Kurtosis: {kurtosis:.1f}')\n",
|
||
"print(f' Jumps (>3σ): {jump_intensity*100:.1f}%')\n",
|
||
"print('=' * 60)\n",
|
||
"\n",
|
||
"# Guardar datos\n",
|
||
"trades.write_csv('../data/binance_btcusdt_trades.csv')\n",
|
||
"ohlcv_1m.write_csv('../data/binance_btcusdt_ohlcv_1m.csv')\n",
|
||
"ob_df.write_csv('../data/binance_btcusdt_orderbook.csv')\n",
|
||
"print(f'\\nDatos guardados en data/')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|