introduced nb/technical_analysis_with_ta.ipynb

notebooks/technical_analysis_with_ta.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt\n",
+    "import yfinance as yf\n",
+    "\n",
+    "ticker = 'TSLA'\n",
+    "period_start = dt.datetime.now() - dt.timedelta(weeks=8)\n",
+    "try:\n",
+    "    print(f'Fetching price data for {ticker}')\n",
+    "    data = yf.download(\n",
+    "        ticker,\n",
+    "        start=period_start,\n",
+    "        end=dt.datetime.now(),\n",
+    "        interval='1d'\n",
+    "    )\n",
+    "except Exception as e:\n",
+    "    print(f'Error fetching price data for {ticker}: {str(e)}')\n",
+    "\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get rid of the redundant ticker column\n",
+    "df= data.copy()\n",
+    "df.columns = df.columns.droplevel('Ticker')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "def plot_stock_metrics_ax(\n",
+    "        ax,\n",
+    "        dataset,\n",
+    "        df,\n",
+    "        colstoplot):\n",
+    "    print(f'plotting {colstoplot} in {dataset}')\n",
+    "    colorcycle = ['black', 'blue', 'red', 'green', 'orange']\n",
+    "    for i, col in enumerate(colstoplot):\n",
+    "        ax.plot(\n",
+    "            df.index,\n",
+    "            df[col],\n",
+    "            color=colorcycle[i],\n",
+    "            label=col,\n",
+    "            linewidth=2)\n",
+    "    # Format major ticks with year\n",
+    "    # Set major ticks (every Monday with labels)\n",
+    "    ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MO))\n",
+    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
+    "    # Set minor ticks (every day, but without labels)\n",
+    "    ax.xaxis.set_minor_locator(mdates.DayLocator())\n",
+    "    \n",
+    "    ax.set_title(dataset)\n",
+    "    ax.set_xlabel('Date')\n",
+    "    ax.set_ylabel(dataset)\n",
+    "    if len(colstoplot) > 1:\n",
+    "        ax.legend()\n",
+    "    if dataset in ['Index', 'Indices']:\n",
+    "        ax.set_ylim([0, 100])\n",
+    "        # Add a transparent shaded region between y=30 and y=70\n",
+    "        ax.fill_between(df.index, 30, 70, color='gray', alpha=0.3)\n",
+    "    if dataset in ['Price', 'Prices']:\n",
+    "        # Add a transparent shaded region between y=30 and y=70\n",
+    "        ax.fill_between(df.index, df['Low'], df['High'], color='gray', alpha=0.3)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "def plot_stock_metrics(\n",
+    "        df,\n",
+    "        datasets={\n",
+    "            'Volume': ['Volume'],\n",
+    "            'Price': ['Close']  # 'High','Low'\n",
+    "            }\n",
+    "            ):\n",
+    "    numax = len(datasets)\n",
+    "    fig, axes = plt.subplots(\n",
+    "        nrows=numax,\n",
+    "        ncols=1,\n",
+    "        figsize=(10, 5*numax))\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        dataset = list(datasets.keys())[i]\n",
+    "        colstoplot = datasets[dataset]\n",
+    "        plot_stock_metrics_ax(\n",
+    "            ax,\n",
+    "            dataset,\n",
+    "            df,\n",
+    "            colstoplot)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "plot_stock_metrics(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ta.volume import volume_weighted_average_price\n",
+    "from ta.momentum import RSIIndicator, StochasticOscillator\n",
+    "from ta.trend import MACD \n",
+    "\n",
+    "# Price Indicators\n",
+    "# Volume-Weighted Average Price (VWAP)\n",
+    "# https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-overlays/volume-weighted-average-price-vwap\n",
+    "df['VWAP'] = volume_weighted_average_price(\n",
+    "    high=df['High'],\n",
+    "    low=df['Low'],\n",
+    "    close=df['Close'],\n",
+    "    volume=df['Volume'],\n",
+    ")\n",
+    "\n",
+    "# Indices\n",
+    "# RSI:\n",
+    "# https://www.investopedia.com/terms/r/rsi.asp\n",
+    "df['RSI'] = RSIIndicator(\n",
+    "    df['Close'],\n",
+    "    window=14).rsi()\n",
+    "# Stochastic Oscillator: \n",
+    "# https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-indicators/stochastic-oscillator-fast-slow-and-full\n",
+    "df['StochOsc'] = StochasticOscillator(\n",
+    "    df['High'],\n",
+    "    df['Low'],\n",
+    "    df['Close'],\n",
+    "    window=14).stoch()\n",
+    "\n",
+    "# Trend signals\n",
+    "# Moving Average Convergence Divergence (MACD):\n",
+    "# https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-indicators/macd-moving-average-convergence-divergence-oscillator\n",
+    "macd = MACD(\n",
+    "    df['Close'],\n",
+    "    window_slow=26,\n",
+    "    window_fast=12,\n",
+    "    window_sign=9)\n",
+    "df['MACD'] = macd.macd()\n",
+    "df['MACDsig'] = macd.macd_signal()\n",
+    "df['MACDdif'] = macd.macd_diff()\n",
+    "\n",
+    "plot_stock_metrics(\n",
+    "    df,\n",
+    "    datasets={\n",
+    "            'Volume': ['Volume'],\n",
+    "            'Prices': ['Close', 'VWAP'],  # 'High','Low', \n",
+    "            'Indices': ['RSI', 'StochOsc'],\n",
+    "            'Trend': ['MACD', 'MACDsig', 'MACDdif']}\n",
+    "            )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "finagents_py311",
+   "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": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}