feat: optim_backtestingpy_sma_with_sl_tp.ipynb

notebooks/optim_backtestingpy_sma_with_sl_tp.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7712680a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%\n",
+    "# filename: optim_backtestingpy_sma_with_sl_tp.ipynb\n",
+    "\n",
+    "# --- 1. Imports and Setup ---\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pandas_ta as ta\n",
+    "from backtesting import Backtest, Strategy\n",
+    "from backtesting.lib import crossover\n",
+    "from backtesting.test import EURUSD\n",
+    "\n",
+    "# --- 2. Load Sample Data ---\n",
+    "print(\"Loading built-in EURUSD sample data...\")\n",
+    "data = EURUSD.copy()\n",
+    "print(\"Data loaded successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fd6f9be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%\n",
+    "# --- 3. The Parameterized Strategy with SL/TP ---\n",
+    "\n",
+    "class SmaCrossSLTP(Strategy):\n",
+    "    # --- Parameters for Optimization ---\n",
+    "    fast_period = 10\n",
+    "    slow_period = 40\n",
+    "\n",
+    "    # New parameters for Stop-Loss and Take-Profit (as percentages)\n",
+    "    # Note: We express them as decimals, e.g., 1.5% SL is 0.015\n",
+    "    sl_percent = 1.5\n",
+    "    tp_percent = 3.0\n",
+    "\n",
+    "    def init(self):\n",
+    "        # Calculate indicators internally for optimization\n",
+    "        self.fast_sma = self.I(ta.sma, pd.Series(self.data.Close), length=self.fast_period)\n",
+    "        self.slow_sma = self.I(ta.sma, pd.Series(self.data.Close), length=self.slow_period)\n",
+    "\n",
+    "    def next(self):\n",
+    "        # Define SL and TP prices based on the current close\n",
+    "        sl_price = self.data.Close[-1] * (1 - self.sl_percent / 100)\n",
+    "        tp_price = self.data.Close[-1] * (1 + self.tp_percent / 100)\n",
+    "\n",
+    "        # Entry logic\n",
+    "        if crossover(self.fast_sma, self.slow_sma):\n",
+    "            if not self.position:\n",
+    "                # When buying, provide the sl and tp prices\n",
+    "                self.buy(sl=sl_price, tp=tp_price)\n",
+    "\n",
+    "        # The exit signal from the crossover is still valid\n",
+    "        elif crossover(self.slow_sma, self.fast_sma):\n",
+    "            self.position.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9572ae93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%\n",
+    "# --- 4. Running the Optimization (FINAL, DEFINITIVE VERSION) ---\n",
+    "print(\"\\n--- Starting Grid Search Optimization with SL/TP ---\")\n",
+    "\n",
+    "bt = Backtest(data, SmaCrossSLTP, cash=10000, commission=.002)\n",
+    "\n",
+    "# Helper function to generate a range of floats\n",
+    "def frange(start, stop, step):\n",
+    "    return np.arange(start, stop, step).tolist()\n",
+    "\n",
+    "# CORRECTED: The optimize() method with return_heatmap=True returns a TUPLE\n",
+    "# containing (stats_series, heatmap_data). We unpack this tuple into two variables.\n",
+    "stats, heatmap = bt.optimize(\n",
+    "    fast_period=range(10, 31, 10),\n",
+    "    slow_period=range(40, 61, 10),\n",
+    "    sl_percent=frange(0.5, 2.1, 0.5),\n",
+    "    tp_percent=frange(2.0, 5.1, 1.0),\n",
+    "\n",
+    "    maximize='SQN',\n",
+    "    constraint=lambda p: p.fast_period < p.slow_period,\n",
+    "    return_heatmap=True # This is crucial for getting the tuple back\n",
+    ")\n",
+    "\n",
+    "print(\"\\n--- Optimization Results ---\")\n",
+    "print(\"Best stats found:\")\n",
+    "# The 'stats' variable now correctly holds the results Series\n",
+    "print(stats)\n",
+    "\n",
+    "print(\"\\nBest parameters found:\")\n",
+    "# The strategy object is an attribute of the stats Series\n",
+    "print(stats._strategy)\n",
+    "\n",
+    "# The 'heatmap' variable now correctly holds the heatmap data for the next cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7cac03e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%\n",
+    "# --- 5. Visualizing the Best Run and Heatmaps ---\n",
+    "from backtesting.lib import plot_heatmaps\n",
+    "\n",
+    "print(\"\\n--- Plotting the Best Strategy Run ---\")\n",
+    "# The .plot() method will automatically show the SL/TP levels on the chart\n",
+    "bt.plot()\n",
+    "\n",
+    "print(\"\\n--- Plotting Optimization Heatmap ---\")\n",
+    "# This will generate a heatmap for each pair of optimized parameters\n",
+    "plot_heatmaps(heatmap, agg='mean')"
+   ]
+  }
+ ],
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+   "name": "python3"
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