Crypto Beta

crypto_beta.ipynb

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    "# Calculating Crypto Beta\n",
    "\n",
    "This should be relatively self explanatory for how to manipulate, in the third cell you can change the symbol of the tokens you want to compare, ETH or BTC is good as your \"index\". Be sure to pick a symbol supported by the exchange you're using, here we use bybit but you can easily switch that out for anything supported by ccxt. We use perp symbols here e.g. `ETHUSDT`, to use `ETH/USDT` form to use spot pairs modify the code in cell 4.\n",
    "\n",
    "To see a deeper explanation of how to use this and what crypto beta is, check out https://www.liquiditygoblin.com"
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     },
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    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "import pandas as pd\n",
    "import ccxt\n",
    "import numpy as np\n",
    "import datetime\n",
    "\n",
    "import scipy.stats as stats\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.models import LinearAxis, Range1d\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from bokeh.models import NumeralTickFormatter\n",
    "\n",
    "output_notebook()"
   ]
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   "cell_type": "code",
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   "id": "f749de05",
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   "outputs": [],
   "source": [
    "def fetch_historical_prices(exchange, symbol, timeframe, start_date, end_date):\n",
    "    # Convert start_date and end_date to timestamps\n",
    "    start_timestamp = int(start_date.timestamp() * 1000)\n",
    "    end_timestamp = int(end_date.timestamp() * 1000)\n",
    "    \n",
    "    # Initialize an empty list to store the fetched OHLCV data\n",
    "    ohlcv_data = []\n",
    "    \n",
    "    # Set up the progress bar\n",
    "    progress_bar = tqdm(total=end_timestamp - start_timestamp, desc='Fetching Data')\n",
    "\n",
    "    # Fetch OHLCV data\n",
    "    current_timestamp = start_timestamp\n",
    "    while current_timestamp < end_timestamp:\n",
    "        # Fetch OHLCV data\n",
    "        data = exchange.fetch_ohlcv(symbol, timeframe, current_timestamp)\n",
    "        if len(data) < 1:\n",
    "            break\n",
    "        # Append the fetched data to the overall list\n",
    "        ohlcv_data += data\n",
    "        # Update the current timestamp and progress bar\n",
    "        progress_bar.update(data[-1][0] + 1 - current_timestamp)\n",
    "\n",
    "        current_timestamp = data[-1][0] + 1  # Set the next timestamp to fetch as the last timestamp in the current data + 1 millisecond\n",
    "    \n",
    "    # Close the progress bar\n",
    "    progress_bar.close()\n",
    "    \n",
    "    # Convert the data into a DataFrame\n",
    "    columns = ['Timestamp', 'Open', 'High', 'Low', 'Close', 'Volume']\n",
    "    df = pd.DataFrame(ohlcv_data, columns=columns)\n",
    "    \n",
    "    # Convert the timestamp to a readable format\n",
    "    df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='ms')\n",
    "    \n",
    "    # Set the Timestamp column as the DataFrame's index\n",
    "    df.set_index('Timestamp', inplace=True)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def calculate_returns(df):\n",
    "    # Calculate the returns as the percentage change in the Close column\n",
    "    df['Return'] = df['Close'].pct_change()\n",
    "    df['Log Return'] = np.log(df['Close']/ df['Close'].shift(-1))\n",
    "\n",
    "    # Drop the first row since it will have NaN as there is no previous close\n",
    "    df = df.dropna()\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b9493cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the exchange\n",
    "exchange = ccxt.bybit()  # Replace with your desired exchange\n",
    "\n",
    "# Define the symbol and timeframe\n",
    "symbol_0 = 'ETH'  # Replace with your desired trading pair\n",
    "symbol_1 = 'MATIC'\n",
    "\n",
    "timeframe = '5m'  # Replace with your desired timeframe (e.g., '1m', '5m', '1h', '1d')\n",
    "\n",
    "n_days = 10  # Replace with how far back you'd like to look\n",
    "\n",
    "# Set the start and end dates\n",
    "end_date = datetime.datetime.now()\n",
    "start_date = end_date - datetime.timedelta(days=n_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ec9e4d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "586483380ae04eccbec7fe1a0b5a32b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching Data:   0%|          | 0/864000000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0aa68da0ace345e7be76b800c83cca63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching Data:   0%|          | 0/864000000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fetch OHLCV data for the specified date range\n",
    "token_0 = fetch_historical_prices(exchange, symbol_0+'USDT', timeframe, start_date, end_date)\n",
    "token_1 = fetch_historical_prices(exchange, symbol_1+'USDT', timeframe, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fc0921ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "token_0 = calculate_returns(token_0)\n",
    "token_1 = calculate_returns(token_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e2fcdc54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter_with_regression(returns_df1, returns_df2):\n",
    "    # Extract the returns values from the DataFrames\n",
    "    returns1 = returns_df1['Return'].values\n",
    "    returns2 = returns_df2['Return'].values\n",
    "    \n",
    "    # Create a new Bokeh figure\n",
    "    p = figure(title='Scatter Plot with Linear Regression', x_axis_label=f'{symbol_0}', y_axis_label=f'{symbol_1}', height=400, width=800)\n",
    "    \n",
    "    # Plot the scatter points\n",
    "    p.scatter(returns1, returns2, size=5, color='navy', alpha=0.2)\n",
    "    \n",
    "    # Perform linear regression\n",
    "    regressor = LinearRegression()\n",
    "    X = returns1.reshape(-1, 1)\n",
    "    y = returns2.reshape(-1, 1)\n",
    "    regressor.fit(X, y)\n",
    "    \n",
    "    # Get the regression line parameters\n",
    "    beta = regressor.coef_[0][0]\n",
    "    alpha = regressor.intercept_[0]\n",
    "    \n",
    "    # Generate the regression line values\n",
    "    line_x = np.linspace(returns1.min(), returns1.max(), 100)\n",
    "    line_y = beta * line_x + alpha\n",
    "    \n",
    "    # Plot the regression line\n",
    "    p.line(line_x, line_y, line_color='red', line_width=2, legend_label=f'Regression Line (Beta={beta:.2f})')\n",
    "    \n",
    "    # Set the range of the y-axis to match the data\n",
    "    min_val = min(returns2.min(), line_y.min())\n",
    "    max_val = max(returns2.max(), line_y.max())\n",
    "    p.y_range = Range1d(min_val, max_val)\n",
    "    \n",
    "    # Add a legend to the plot\n",
    "    p.legend.location = 'top_left'\n",
    "    \n",
    "    # Show the plot\n",
    "    show(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0762234c",
   "metadata": {},
   "outputs": [
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Application\",\"version\":\"2.4.3\"}};\n",
       "  const render_items = [{\"docid\":\"cc79881d-ba75-44cc-9b77-a38058dd92ee\",\"root_ids\":[\"1174\"],\"roots\":{\"1174\":\"274f0c92-2134-4c7c-8ff9-805be738c6a6\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1174"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_with_regression(token_0, token_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a094554",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}