Initial commit

2025-02-02 18:22:12 -05:00
commit 17e5d2a1e1
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Rolling Window Market Movement Analysis\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Fetched 316 rows of data.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import sys\n",
+        "sys.path.append('../')\n",
+        "import asyncio\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from datetime import datetime, timedelta\n",
+        "from IPython.display import display\n",
+        "from tqdm.notebook import tqdm\n",
+        "\n",
+        "from market_predictor.market_data_fetcher import MarketDataFetcher\n",
+        "from market_predictor.data_processor import MarketDataProcessor\n",
+        "from market_predictor.rag_engine import RAGEngine\n",
+        "from market_predictor.performance_metrics import PerformanceMetrics\n",
+        "from market_predictor.prediction_service import PredictionService\n",
+        "\n",
+        "\n",
+        "symbol = \"BTC-USD\"\n",
+        "end_date = datetime.now()\n",
+        "start_date = end_date - timedelta(days=5)\n",
+        "fetcher = MarketDataFetcher(symbol)\n",
+        "market_data = fetcher.fetch_data(\n",
+        "    start_date=start_date.strftime('%Y-%m-%d'),\n",
+        "    end_date=end_date.strftime('%Y-%m-%d'),\n",
+        "    interval='5m'\n",
+        ")\n",
+        "print(f\"Fetched {len(market_data)} rows of data.\")\n",
+        "\n",
+        "rag_engine = RAGEngine()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define Rolling Window Prediction Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Processing:  58%|████████████████▉            | 142/244 [10:00<18:10, 10.69s/it]"
+          ]
+        }
+      ],
+      "source": [
+        "import asyncio\n",
+        "import pandas as pd\n",
+        "from tqdm import tqdm\n",
+        "import nest_asyncio\n",
+        "\n",
+        "# Enable nested event loops\n",
+        "nest_asyncio.apply()\n",
+        "\n",
+        "async def analyze_market_data(\n",
+        "    market_data: pd.DataFrame,\n",
+        "    training_window_size: int = 36,\n",
+        "    inference_window_size: int = 12,\n",
+        "    inference_offset: int = 0\n",
+        ") -> pd.DataFrame:\n",
+        "    processor = MarketDataProcessor(market_data)\n",
+        "    processed_data = processor.df\n",
+        "    \n",
+        "    service = PredictionService(\n",
+        "        market_data=processed_data,\n",
+        "        training_window_size=training_window_size,\n",
+        "        inference_window_size=inference_window_size,\n",
+        "        inference_offset=inference_offset\n",
+        "    )\n",
+        "    \n",
+        "    total_size = training_window_size + inference_offset + inference_window_size\n",
+        "    total_windows = len(processed_data) - total_size\n",
+        "    \n",
+        "    predictions = []\n",
+        "    with tqdm(total=total_windows, desc=\"Processing\", ncols=80) as pbar:\n",
+        "        async for pred in service.generate_rolling_predictions():\n",
+        "            if pred:\n",
+        "                # print(pred)\n",
+        "                predictions.append(pred)\n",
+        "                pbar.update(1)\n",
+        "    \n",
+        "    return pd.DataFrame(predictions) if predictions else pd.DataFrame()\n",
+        "\n",
+        "# Run analysis\n",
+        "try:\n",
+        "    predictions_df = await analyze_market_data(\n",
+        "        market_data,\n",
+        "        training_window_size=60,\n",
+        "        inference_window_size=12,\n",
+        "        inference_offset=0\n",
+        "    )\n",
+        "    \n",
+        "    if not predictions_df.empty:\n",
+        "        metrics = PerformanceMetrics(predictions_df, market_data)\n",
+        "        report = metrics.generate_report()\n",
+        "        print(\"\\nPerformance Report:\")\n",
+        "        print(report)\n",
+        "       \n",
+        "        print(\"\\nPredictions Summary:\")\n",
+        "        print(predictions_df.head())\n",
+        "    else:\n",
+        "        print(\"No predictions generated\")\n",
+        "        \n",
+        "except Exception as e:\n",
+        "    print(f\"Analysis failed: {str(e)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "DeepSeek Playground (Poetry)",
+      "language": "python",
+      "name": "deepseek_playground"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}