ferro-ta/examples/backtesting.ipynb

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 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Backtesting with ferro-ta\n",
    "\n",
    "This notebook demonstrates the minimal backtesting harness and the\n",
    "indicator pipeline, together with the configuration defaults API.\n",
    "\n",
    "Install:\n",
    "```bash\n",
    "pip install ferro-ta\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ferro_ta.config as config\n",
    "import numpy as np\n",
    "from ferro_ta.backtest import backtest\n",
    "from ferro_ta.pipeline import Pipeline\n",
    "\n",
    "from ferro_ta import BBANDS, EMA, RSI, SMA\n",
    "\n",
    "# Synthetic data\n",
    "np.random.seed(42)\n",
    "n = 300\n",
    "close = np.cumprod(1 + np.random.randn(n) * 0.01) * 100\n",
    "volume = np.random.randint(1000, 10000, n).astype(float)\n",
    "print(f\"Generated {n} bars, final price: {close[-1]:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RSI 30/70 Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = backtest(close, strategy=\"rsi_30_70\", timeperiod=14)\n",
    "print(\"Strategy: RSI 30/70\")\n",
    "print(f\"Final equity: {result.final_equity:.4f}\")\n",
    "print(f\"Number of trades: {result.n_trades}\")\n",
    "print(f\"Return: {(result.final_equity - 1.0) * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMA Crossover Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result2 = backtest(close, strategy=\"sma_crossover\", fast=10, slow=30)\n",
    "print(\"Strategy: SMA Crossover (10/30)\")\n",
    "print(f\"Final equity: {result2.final_equity:.4f}\")\n",
    "print(f\"Number of trades: {result2.n_trades}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration Defaults\n",
    "\n",
    "Set global defaults for indicator parameters to avoid repeating them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set global defaults\n",
    "config.set_default(\"timeperiod\", 20)  # global default for all indicators\n",
    "config.set_default(\"RSI.timeperiod\", 14)  # RSI-specific override\n",
    "\n",
    "print(\"Current defaults:\", config.list_defaults())\n",
    "print(\"RSI defaults:\", config.get_defaults_for(\"RSI\"))\n",
    "print(\"SMA defaults:\", config.get_defaults_for(\"SMA\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Context manager for temporary overrides\n",
    "with config.Config(timeperiod=5):\n",
    "    temp_default = config.get_default(\"timeperiod\")\n",
    "    print(f\"Inside context: timeperiod={temp_default}\")\n",
    "\n",
    "print(f\"After context: timeperiod={config.get_default('timeperiod')}\")  # back to 20\n",
    "\n",
    "# Clean up\n",
    "config.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-indicator Pipeline for Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = (\n",
    "    Pipeline()\n",
    "    .add(\"sma_10\", SMA, timeperiod=10)\n",
    "    .add(\"sma_30\", SMA, timeperiod=30)\n",
    "    .add(\"ema_10\", EMA, timeperiod=10)\n",
    "    .add(\"rsi_14\", RSI, timeperiod=14)\n",
    "    .add(\n",
    "        \"bb\",\n",
    "        BBANDS,\n",
    "        output_keys=[\"bb_upper\", \"bb_mid\", \"bb_lower\"],\n",
    "        timeperiod=20,\n",
    "        nbdevup=2.0,\n",
    "        nbdevdn=2.0,\n",
    "    )\n",
    ")\n",
    "\n",
    "features = pipe.run(close)\n",
    "print(\"Feature columns:\", list(features.keys()))\n",
    "\n",
    "# Build a simple feature matrix (last 5 complete rows)\n",
    "valid_start = 30  # warmup\n",
    "feature_matrix = np.column_stack([v[valid_start:] for v in features.values()])\n",
    "print(f\"Feature matrix shape: {feature_matrix.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Manual Backtest Using the Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Signal: long when RSI < 40 AND close > SMA_30; flat otherwise\n",
    "rsi_vals = features[\"rsi_14\"]\n",
    "sma30_vals = features[\"sma_30\"]\n",
    "\n",
    "signal = np.where((rsi_vals < 40) & (close > sma30_vals), 1.0, 0.0)\n",
    "position = np.roll(signal, 1)  # trade on next bar open\n",
    "position[0] = 0.0\n",
    "\n",
    "returns = np.diff(close) / close[:-1]\n",
    "strategy_returns = returns * position[1:]\n",
    "\n",
    "equity = np.cumprod(1 + strategy_returns)\n",
    "print(f\"Final equity: {equity[-1]:.4f}\")\n",
    "print(f\"Number of signal bars: {int(signal.sum())}\")"
   ]
  }
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