un-oracle-nigmatique.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/dascient/un-oracle-nigmatique?scriptVersionId=216325560\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"# Un Oracle Énigmatique\n## A Debatably Profitable Autonomous Tradable Robotical Solvant, I mean Solution..,m\n\nThe code above integrates a Kalman Filter-based trend estimation to generate autonomous tradinğ signals for any åsset, including çryptocurrencies.\n\n# Welcome to our Universe\n## [@donutz.ai](www.donutz.ai/#)","metadata":{}},{"cell_type":"code","source":"from IPython.display import clear_output\n!pip install pykalman\n!pip install yfinance\nclear_output()","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2025-01-04T07:51:42.548059Z","iopub.execute_input":"2025-01-04T07:51:42.54846Z","iopub.status.idle":"2025-01-04T07:51:50.703251Z","shell.execute_reply.started":"2025-01-04T07:51:42.548426Z","shell.execute_reply":"2025-01-04T07:51:50.701727Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport yfinance as yf\nfrom pykalman import KalmanFilter\nimport matplotlib.pyplot as plt\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Function to fetch historical data\ndef fetch_data(ticker, start_date, end_date):\n    print(\"Summoning the market's whispers...\")\n    data = yf.download(ticker, start=start_date, end=end_date)\n    data = pd.read_csv('https://www.cryptodatadownload.com/cdd/Binance_SHIBUSDT_1h.csv',header=1)\n    data = data.tail(300)\n    data['Return'] = data['Close'].pct_change()\n    return data\n\n# Kalman Filter for trend estimation\ndef kalman_filter(prices):\n    print(\"Unraveling the veils of uncertainty...\")\n    kf = KalmanFilter(transition_matrices=[1], observation_matrices=[1],\n                      initial_state_mean=prices[0], n_dim_obs=1)\n    state_means, _ = kf.filter(prices)\n    return state_means\n\n# Trading signals based on Kalman Filter trend\ndef generate_signals(data):\n    print(\"Deciphering cryptic signals...\")\n    trend = kalman_filter(data['Close'].values)\n    data['Trend'] = trend\n    data['Signal'] = 0\n    data.loc[data['Close'] > data['Trend'], 'Signal'] = 1  # Buy\n    data.loc[data['Close'] < data['Trend'], 'Signal'] = -1  # Sell\n    return data\n\n# Backtest the strategy\ndef backtest(data, initial_capital):\n    print(\"Traversing the labyrinth of profits...\")\n    data['Portfolio'] = initial_capital\n    for i in range(1, len(data)):\n        data.loc[data.index[i], 'Portfolio'] = (\n            data['Portfolio'].iloc[i - 1] * (1 + data['Return'].iloc[i])\n            if data['Signal'].iloc[i - 1] == 1 else data['Portfolio'].iloc[i - 1]\n        )\n    return data\n\n# Visualization\ndef plot_results(data, ticker):\n    print(\"Revealing the hidden patterns...\")\n    plt.figure(figsize=(24, 7))\n    plt.plot(data['Close'], label='Price', alpha=0.666)\n    plt.plot(data['Trend'], label='Kalman Trend', linestyle='--')\n    plt.scatter(data.index, data['Close'][data['Signal'] == 1], label='Buy Signal', marker='^', color='green')\n    plt.scatter(data.index, data['Close'][data['Signal'] == -1], label='Sell Signal', marker='v', color='red')\n    plt.title(f\"{ticker} - Autonomous Trading Signals\")\n    plt.legend()\n    plt.show()\n\n# Main function\ndef main():\n    ticker = \"SHIB-USD\"  # Choose your asset\n    start_date = \"2024-06-01\"\n    end_date = \"2025-01-01\"\n    initial_capital = 10000\n\n    print(\"Awakening the algorithmic sage...\")\n    data = fetch_data(ticker, start_date, end_date)\n    data = generate_signals(data)\n    data = backtest(data, initial_capital)\n\n    print(f\"Final Portfolio Value: ${data['Portfolio'].iloc[-1]:.2f}\")\n    plot_results(data, ticker)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-04T08:01:37.606385Z","iopub.execute_input":"2025-01-04T08:01:37.606746Z","iopub.status.idle":"2025-01-04T08:01:37.620177Z","shell.execute_reply.started":"2025-01-04T08:01:37.606717Z","shell.execute_reply":"2025-01-04T08:01:37.618707Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")\n\nmain()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-04T08:01:39.207732Z","iopub.execute_input":"2025-01-04T08:01:39.208262Z","iopub.status.idle":"2025-01-04T08:01:41.946349Z","shell.execute_reply.started":"2025-01-04T08:01:39.208207Z","shell.execute_reply":"2025-01-04T08:01:41.944645Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# en fin, din-din, ma tin mints, & cools goons balloons ain't for fools init, gov'nah!","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}