daily update

research/data/archive/OUTDATED_ohlc_persistance_structure.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Test persistance\n",
+    "# Experiment with file persistence structure\n",
     "\n",
     "ohlcv and trades persistence with bar type and trade filtering and minsize support\n",
     "\n",

research/test1.ipynb

@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import statsmodels.api as sm\n",
-    "\n",
-    "# Example time series data\n",
-    "np.random.seed(0)\n",
-    "dates = pd.date_range('2023-01-01', periods=100)\n",
-    "data = pd.Series(np.random.randn(100).cumsum(), index=dates)\n",
-    "\n",
-    "# Parameters\n",
-    "window_size = 20\n",
-    "\n",
-    "# Function to calculate rolling window linear regression\n",
-    "def rolling_linreg(series, window):\n",
-    "    intercepts = []\n",
-    "    slopes = []\n",
-    "    for i in range(len(series) - window + 1):\n",
-    "        y = series[i:i + window]\n",
-    "        x = np.arange(window)\n",
-    "        x = sm.add_constant(x)\n",
-    "        model = sm.OLS(y, x).fit()\n",
-    "        intercepts.append(model.params[0])\n",
-    "        slopes.append(model.params[1])\n",
-    "    return intercepts, slopes\n",
-    "\n",
-    "# Calculate rolling linear regression parameters\n",
-    "intercepts, slopes = rolling_linreg(data, window_size)\n",
-    "\n",
-    "# Create a DataFrame for plotting\n",
-    "rolling_dates = dates[window_size - 1:]\n",
-    "rolling_intercepts = pd.Series(intercepts, index=rolling_dates)\n",
-    "rolling_slopes = pd.Series(slopes, index=rolling_dates)\n",
-    "\n",
-    "# Plot the original data and the rolling linear regression\n",
-    "plt.figure(figsize=(14, 7))\n",
-    "plt.plot(data, label='Original Data')\n",
-    "for i in range(len(rolling_intercepts)):\n",
-    "    start_date = rolling_dates[i] - pd.DateOffset(days=window_size-1)\n",
-    "    end_date = rolling_dates[i]\n",
-    "    plt.plot([start_date, end_date],\n",
-    "             [rolling_intercepts[i], rolling_intercepts[i] + rolling_slopes[i] * (window_size - 1)],\n",
-    "             color='red', alpha=0.5)\n",
-    "\n",
-    "plt.legend()\n",
-    "plt.title('Rolling Window Linear Regression')\n",
-    "plt.xlabel('Date')\n",
-    "plt.ylabel('Value')\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}