{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenClaw Trading - Getting Started\n",
    "\n",
    "This notebook introduces the basics of OpenClaw Trading.\n",
    "\n",
    "## What You'll Learn\n",
    "\n",
    "- How to create and use an economic tracker\n",
    "- How to simulate trades and track performance\n",
    "- How to check survival status\n",
    "- How to visualize balance history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "import sys\n",
    "sys.path.insert(0, '../src')\n",
    "\n",
    "from openclaw.core.economy import TradingEconomicTracker\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "print(\"✓ Imports successful\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Creating an Economic Tracker\n",
    "\n",
    "The `TradingEconomicTracker` is the core component for tracking agent finances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an economic tracker\n",
    "tracker = TradingEconomicTracker(\n",
    "    agent_id=\"tutorial_agent\",\n",
    "    initial_capital=1000.0\n",
    ")\n",
    "\n",
    "print(f\"Agent ID: {tracker.agent_id}\")\n",
    "print(f\"Initial Capital: ${tracker.initial_capital:,.2f}\")\n",
    "print(f\"Current Balance: ${tracker.balance:,.2f}\")\n",
    "print(f\"Status: {tracker.get_survival_status().value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Simulating Decisions and Trades\n",
    "\n",
    "Agents pay for decisions (API calls, analysis) and trades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some decisions (analysis costs)\n",
    "for i in range(5):\n",
    "    cost = tracker.calculate_decision_cost(\n",
    "        tokens_input=1000,\n",
    "        tokens_output=500,\n",
    "        market_data_calls=2\n",
    "    )\n",
    "    print(f\"Decision {i+1} cost: ${cost:.4f}\")\n",
    "\n",
    "print(f\"\\nBalance after decisions: ${tracker.balance:,.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some trades\n",
    "import random\n",
    "\n",
    "for i in range(10):\n",
    "    is_win = i % 2 == 0  # Alternate wins and losses\n",
    "    win_amount = 20.0 if is_win else -10.0\n",
    "    \n",
    "    result = tracker.calculate_trade_cost(\n",
    "        trade_value=100.0,\n",
    "        is_win=is_win,\n",
    "        win_amount=win_amount\n",
    "    )\n",
    "    \n",
    "    print(f\"Trade {i+1}: {\"Win\" if is_win else \"Loss\"} ${win_amount:+.2f} | Balance: ${result.balance:,.2f}\")\n",
    "\n",
    "print(f\"\\nFinal Balance: ${tracker.balance:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Checking Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get current status\n",
    "status = tracker.get_survival_status()\n",
    "\n",
    "print(\"Performance Summary\")\n",
    "print(\"=\" * 40)\n",
    "print(f\"Status: {status.value}\")\n",
    "print(f\"Initial Capital: ${tracker.initial_capital:,.2f}\")\n",
    "print(f\"Current Balance: ${tracker.balance:,.2f}\")\n",
    "print(f\"Total Return: {(tracker.balance/tracker.initial_capital - 1)*100:+.2f}%\")\n",
    "print(f\"\\nCosts:\")\n",
    "print(f\"  Token Costs: ${tracker.token_costs:.4f}\")\n",
    "print(f\"  Trade Costs: ${tracker.trade_costs:.4f}\")\n",
    "print(f\"  Total Costs: ${tracker.total_costs:.4f}\")\n",
    "print(f\"\\nProfit/Loss: ${tracker.net_profit:+.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualizing Balance History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get balance history\n",
    "history = tracker.get_balance_history()\n",
    "\n",
    "# Convert to DataFrame for easy plotting\n",
    "df = pd.DataFrame([\n",
    "    {\n",
    "        'timestamp': entry.timestamp,\n",
    "        'balance': entry.balance,\n",
    "        'change': entry.change\n",
    "    }\n",
    "    for entry in history\n",
    "])\n",
    "\n",
    "# Plot balance over time\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df['timestamp'], df['balance'], marker='o')\n",
    "plt.axhline(y=tracker.initial_capital, color='r', linestyle='--', label='Initial Capital')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Balance ($)')\n",
    "plt.title('Agent Balance History')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Total entries: {len(df)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Experimenting with Different Scenarios\n",
    "\n",
    "Let's create multiple agents with different strategies and compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create three different agents\n",
    "agents = {\n",
    "    'Conservative': TradingEconomicTracker('conservative', 1000.0),\n",
    "    'Balanced': TradingEconomicTracker('balanced', 1000.0),\n",
    "    'Aggressive': TradingEconomicTracker('aggressive', 1000.0)\n",
    "}\n",
    "\n",
    "# Simulate different strategies\n",
    "for name, agent in agents.items():\n",
    "    for i in range(20):\n",
    "        if name == 'Conservative':\n",
    "            # Conservative: small wins, small losses\n",
    "            is_win = i % 3 == 0  # 33% win rate\n",
    "            amount = 5.0 if is_win else -3.0\n",
    "        elif name == 'Balanced':\n",
    "            # Balanced: medium wins/losses\n",
    "            is_win = i % 2 == 0  # 50% win rate\n",
    "            amount = 10.0 if is_win else -8.0\n",
    "        else:  # Aggressive\n",
    "            # Aggressive: large wins/losses\n",
    "            is_win = i % 2 == 0  # 50% win rate\n",
    "            amount = 25.0 if is_win else -20.0\n",
    "        \n",
    "        agent.calculate_trade_cost(100.0, is_win, amount)\n",
    "\n",
    "# Compare results\n",
    "print(\"Strategy Comparison\")\n",
    "print(\"=\" * 60)\n",
    "for name, agent in agents.items():\n",
    "    return_pct = (agent.balance/agent.initial_capital - 1) * 100\n",
    "    print(f\"{name:15} | Balance: ${agent.balance:>8,.2f} | Return: {return_pct:+6.2f}% | Status: {agent.get_survival_status().value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot comparison\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "for name, agent in agents.items():\n",
    "    history = agent.get_balance_history()\n",
    "    balances = [entry.balance for entry in history]\n",
    "    plt.plot(balances, label=name, marker='o', markersize=3)\n",
    "\n",
    "plt.axhline(y=1000, color='black', linestyle='--', alpha=0.5, label='Initial')\n",
    "plt.xlabel('Trade Number')\n",
    "plt.ylabel('Balance ($)')\n",
    "plt.title('Strategy Comparison')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "- Learn about the workflow system in `02_workflow_demo.ipynb`\n",
    "- Explore backtesting in `03_backtesting.ipynb`\n",
    "- Create custom strategies in `04_custom_strategies.ipynb`"
   ]
  }
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