power-poly.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "28298832-c801-488c-94ab-ae4fe180f8b3",
   "metadata": {},
   "source": [
    "# Power Laws"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10ebe94-7ac6-4b8a-80eb-32c9ac06899b",
   "metadata": {
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   },
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6cded81-ff35-4302-a4cf-c9af0ae178d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-31 20:22:10 INFO     Fetching BTC ReferenceRate...\n",
      "2024-08-31 20:22:12 INFO     Sleeping for a rate limit window because 429 (too many requests) error was returned. Pleasesee Coin Metrics APIV4 documentation for more information: https://docs.coinmetrics.io/api/v4/#tag/Rate-limits\n",
      "2024-08-31 20:22:20 INFO     Sleeping for a rate limit window because 429 (too many requests) error was returned. Pleasesee Coin Metrics APIV4 documentation for more information: https://docs.coinmetrics.io/api/v4/#tag/Rate-limits\n",
      "2024-08-31 20:22:28 INFO     Sleeping for a rate limit window because 429 (too many requests) error was returned. Pleasesee Coin Metrics APIV4 documentation for more information: https://docs.coinmetrics.io/api/v4/#tag/Rate-limits\n",
      "2024-08-31 20:22:36 INFO     Sleeping for a rate limit window because 429 (too many requests) error was returned. Pleasesee Coin Metrics APIV4 documentation for more information: https://docs.coinmetrics.io/api/v4/#tag/Rate-limits\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-09-01 00:00:00+00:00</th>\n",
       "      <td>btc</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-02 00:00:00+00:00</th>\n",
       "      <td>btc</td>\n",
       "      <td>0.062025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-03 00:00:00+00:00</th>\n",
       "      <td>btc</td>\n",
       "      <td>0.0634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-04 00:00:00+00:00</th>\n",
       "      <td>btc</td>\n",
       "      <td>0.06085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-05 00:00:00+00:00</th>\n",
       "      <td>btc</td>\n",
       "      <td>0.062178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          asset     Close\n",
       "time                                     \n",
       "2010-09-01 00:00:00+00:00   btc      0.06\n",
       "2010-09-02 00:00:00+00:00   btc  0.062025\n",
       "2010-09-03 00:00:00+00:00   btc    0.0634\n",
       "2010-09-04 00:00:00+00:00   btc   0.06085\n",
       "2010-09-05 00:00:00+00:00   btc  0.062178"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from coinmetrics.api_client import CoinMetricsClient\n",
    "import logging\n",
    "from scipy.stats import linregress, lognorm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Configure logging\n",
    "logging.basicConfig(\n",
    "    format='%(asctime)s %(levelname)-8s %(message)s',\n",
    "    level=logging.INFO,\n",
    "    datefmt='%Y-%m-%d %H:%M:%S'\n",
    ")\n",
    "\n",
    "# Initialize Coin Metrics API client\n",
    "client = CoinMetricsClient()\n",
    "\n",
    "# Define the asset, metric, and time range\n",
    "asset = 'btc'\n",
    "metric = 'ReferenceRate'\n",
    "start_time = '2010-09-01'\n",
    "end_time = '2024-08-31'\n",
    "frequency = '1d'\n",
    "\n",
    "# Fetch the metric data for the specified asset and time range\n",
    "logging.info(\"Fetching BTC ReferenceRate...\")\n",
    "df = client.get_asset_metrics(\n",
    "    assets=asset,\n",
    "    metrics=[metric],\n",
    "    frequency=frequency,\n",
    "    start_time=start_time,\n",
    "    end_time=end_time\n",
    ").to_dataframe()\n",
    "\n",
    "# Rename the 'ReferenceRate' column to 'Close'\n",
    "df = df.rename(columns={metric: 'Close'})\n",
    "\n",
    "# Set 'time' as the index\n",
    "df['time'] = pd.to_datetime(df['time'])\n",
    "df.set_index('time', inplace=True)\n",
    "\n",
    "# Display the first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1691c9fe-b7d3-454f-a958-0b29651aafcb",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Create Days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f2f4f39-6608-42d7-b815-fe58b299205d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-09-02</th>\n",
       "      <td>0.062025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-03</th>\n",
       "      <td>0.0634</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-04</th>\n",
       "      <td>0.06085</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-05</th>\n",
       "      <td>0.062178</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-06</th>\n",
       "      <td>0.06165</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-27</th>\n",
       "      <td>62983.442773</td>\n",
       "      <td>5109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-28</th>\n",
       "      <td>59548.007622</td>\n",
       "      <td>5110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-29</th>\n",
       "      <td>59100.774755</td>\n",
       "      <td>5111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-30</th>\n",
       "      <td>59339.790995</td>\n",
       "      <td>5112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31</th>\n",
       "      <td>59142.939964</td>\n",
       "      <td>5113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5113 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Close  Days\n",
       "time                          \n",
       "2010-09-02      0.062025     1\n",
       "2010-09-03        0.0634     2\n",
       "2010-09-04       0.06085     3\n",
       "2010-09-05      0.062178     4\n",
       "2010-09-06       0.06165     5\n",
       "...                  ...   ...\n",
       "2024-08-27  62983.442773  5109\n",
       "2024-08-28  59548.007622  5110\n",
       "2024-08-29  59100.774755  5111\n",
       "2024-08-30  59339.790995  5112\n",
       "2024-08-31  59142.939964  5113\n",
       "\n",
       "[5113 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure 'start_date' is timezone-naive\n",
    "start_date = pd.Timestamp(start_time).tz_localize(None)\n",
    "\n",
    "# Ensure the index of df is timezone-naive\n",
    "df.index = df.index.tz_localize(None)\n",
    "\n",
    "# Create 'Days' column\n",
    "df['Days'] = (df.index - start_date).days\n",
    "df = df.tail(-1)\n",
    "\n",
    "df = df.drop(columns=['asset'])\n",
    "# Convert the 'Close' and 'Days' columns to numeric types, coercing errors to NaN\n",
    "df['Close'] = pd.to_numeric(df['Close'], errors='coerce')\n",
    "df['Days'] = pd.to_numeric(df['Days'], errors='coerce')\n",
    "\n",
    "df = df.dropna(subset=['Close', 'Days'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1c6b87-087f-4197-9587-2a9aaa6910e0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Create log_days and log_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6836b5b2-3239-41ff-a36e-b0a68c927749",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Days</th>\n",
       "      <th>log_days</th>\n",
       "      <th>log_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-09-02</th>\n",
       "      <td>0.062025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.780221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-03</th>\n",
       "      <td>0.063400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>-2.758291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-04</th>\n",
       "      <td>0.060850</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>-2.799343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-05</th>\n",
       "      <td>0.062178</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>-2.777750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-06</th>\n",
       "      <td>0.061650</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>-2.786282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-27</th>\n",
       "      <td>62983.442773</td>\n",
       "      <td>5109.0</td>\n",
       "      <td>8.538759</td>\n",
       "      <td>11.050627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-28</th>\n",
       "      <td>59548.007622</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>8.538955</td>\n",
       "      <td>10.994538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-29</th>\n",
       "      <td>59100.774755</td>\n",
       "      <td>5111.0</td>\n",
       "      <td>8.539150</td>\n",
       "      <td>10.986999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-30</th>\n",
       "      <td>59339.790995</td>\n",
       "      <td>5112.0</td>\n",
       "      <td>8.539346</td>\n",
       "      <td>10.991035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31</th>\n",
       "      <td>59142.939964</td>\n",
       "      <td>5113.0</td>\n",
       "      <td>8.539542</td>\n",
       "      <td>10.987713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5113 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Close    Days  log_days  log_close\n",
       "time                                                 \n",
       "2010-09-02      0.062025     1.0  0.000000  -2.780221\n",
       "2010-09-03      0.063400     2.0  0.693147  -2.758291\n",
       "2010-09-04      0.060850     3.0  1.098612  -2.799343\n",
       "2010-09-05      0.062178     4.0  1.386294  -2.777750\n",
       "2010-09-06      0.061650     5.0  1.609438  -2.786282\n",
       "...                  ...     ...       ...        ...\n",
       "2024-08-27  62983.442773  5109.0  8.538759  11.050627\n",
       "2024-08-28  59548.007622  5110.0  8.538955  10.994538\n",
       "2024-08-29  59100.774755  5111.0  8.539150  10.986999\n",
       "2024-08-30  59339.790995  5112.0  8.539346  10.991035\n",
       "2024-08-31  59142.939964  5113.0  8.539542  10.987713\n",
       "\n",
       "[5113 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Convert 'Days' to Float64\n",
    "df['Days'] = pd.to_numeric(df['Days'], errors='coerce').astype('float64')\n",
    "df['Close'] = pd.to_numeric(df['Close'], errors='coerce').astype('float64')\n",
    "\n",
    "df['log_days'] = np.log(df['Days'])\n",
    "df['log_close'] = np.log(df['Close'])\n",
    "\n",
    "# Convert 'log_days' to Float64\n",
    "df['log_days'] = pd.to_numeric(df['log_days'], errors='coerce').astype('float64')\n",
    "df['log_close'] = pd.to_numeric(df['log_close'], errors='coerce').astype('float64')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10116030-8f3b-416f-a6c5-6b0dee917e55",
   "metadata": {},
   "source": [
    "## Fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf67ab2-5d7e-4ceb-b701-e942c5077af7",
   "metadata": {},
   "source": [
    "### Polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcb684e3-b3cf-47d6-beb4-2c4242420d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(               Close   Days  log_days  log_close  log_fitted    fitted\n",
       " time                                                                  \n",
       " 2011-09-02  8.234282  366.0  5.902633   2.108306    1.290595  3.634949\n",
       " 2011-09-03  8.621424  367.0  5.905362   2.154250    1.297158  3.658885\n",
       " 2011-09-04  8.449653  368.0  5.908083   2.134125    1.303710  3.682936\n",
       " 2011-09-05  8.203471  369.0  5.910797   2.104557    1.310251  3.707104\n",
       " 2011-09-06  7.597333  370.0  5.913503   2.027797    1.316781  3.731389,\n",
       " 0.9387032677223787)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import RANSACRegressor, LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Prepare the data for fitting\n",
    "X = df['log_days'].values.reshape(-1, 1)  # Reshape for sklearn\n",
    "y = df['log_close'].values\n",
    "\n",
    "# Set up the RANSAC regressor with a cubic polynomial model\n",
    "ransac = RANSACRegressor(\n",
    "    estimator=make_pipeline(PolynomialFeatures(degree=3), LinearRegression()),\n",
    "    min_samples=0.9, \n",
    "    residual_threshold=5.0, \n",
    "    random_state=0\n",
    ")\n",
    "\n",
    "# Fit the model\n",
    "ransac.fit(X, y)\n",
    "\n",
    "# Predict the fitted values (in log space) using the model and add them to the DataFrame\n",
    "df['log_fitted'] = ransac.predict(X)\n",
    "df['fitted'] = np.exp(df['log_fitted'])\n",
    "\n",
    "# Drop the first year's worth of data\n",
    "df = df[df.index >= df.index[0] + pd.DateOffset(years=1)]\n",
    "\n",
    "# Recompute X and y after dropping rows\n",
    "X = df['log_days'].values.reshape(-1, 1)\n",
    "y = df['log_close'].values\n",
    "\n",
    "# Calculate the R-squared value\n",
    "r_value = r2_score(y, df['log_fitted'])\n",
    "\n",
    "# Display the first few rows of the resulting DataFrame and the R-squared value\n",
    "df.head(), r_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9638e2a-61f8-4fae-ab43-8975fb2b1052",
   "metadata": {},
   "source": [
    "## Calculate upper and lower bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "631af556-27c1-494e-a2c8-3e5f08a8e63a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/1424972503.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['residuals'] = df['log_close'] - df['log_fitted']\n",
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/1424972503.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['upper_bound'] = np.exp(df['log_fitted'] + deviation_scale * lognorm.ppf(0.34, shape, loc=loc, scale=scale))\n",
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/1424972503.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['lower_bound'] = np.exp(df['log_fitted'] - deviation_scale * lognorm.ppf(0.34, shape, loc=loc, scale=scale))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Days</th>\n",
       "      <th>log_days</th>\n",
       "      <th>log_close</th>\n",
       "      <th>log_fitted</th>\n",
       "      <th>fitted</th>\n",
       "      <th>residuals</th>\n",
       "      <th>upper_bound</th>\n",
       "      <th>lower_bound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-02</th>\n",
       "      <td>8.234282</td>\n",
       "      <td>366.0</td>\n",
       "      <td>5.902633</td>\n",
       "      <td>2.108306</td>\n",
       "      <td>1.290595</td>\n",
       "      <td>3.634949</td>\n",
       "      <td>0.817711</td>\n",
       "      <td>8.906500</td>\n",
       "      <td>1.483507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-03</th>\n",
       "      <td>8.621424</td>\n",
       "      <td>367.0</td>\n",
       "      <td>5.905362</td>\n",
       "      <td>2.154250</td>\n",
       "      <td>1.297158</td>\n",
       "      <td>3.658885</td>\n",
       "      <td>0.857092</td>\n",
       "      <td>8.965147</td>\n",
       "      <td>1.493276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-04</th>\n",
       "      <td>8.449653</td>\n",
       "      <td>368.0</td>\n",
       "      <td>5.908083</td>\n",
       "      <td>2.134125</td>\n",
       "      <td>1.303710</td>\n",
       "      <td>3.682936</td>\n",
       "      <td>0.830415</td>\n",
       "      <td>9.024079</td>\n",
       "      <td>1.503092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-05</th>\n",
       "      <td>8.203471</td>\n",
       "      <td>369.0</td>\n",
       "      <td>5.910797</td>\n",
       "      <td>2.104557</td>\n",
       "      <td>1.310251</td>\n",
       "      <td>3.707104</td>\n",
       "      <td>0.794306</td>\n",
       "      <td>9.083296</td>\n",
       "      <td>1.512955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-06</th>\n",
       "      <td>7.597333</td>\n",
       "      <td>370.0</td>\n",
       "      <td>5.913503</td>\n",
       "      <td>2.027797</td>\n",
       "      <td>1.316781</td>\n",
       "      <td>3.731389</td>\n",
       "      <td>0.711017</td>\n",
       "      <td>9.142800</td>\n",
       "      <td>1.522866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-27</th>\n",
       "      <td>62983.442773</td>\n",
       "      <td>5109.0</td>\n",
       "      <td>8.538759</td>\n",
       "      <td>11.050627</td>\n",
       "      <td>11.187657</td>\n",
       "      <td>72233.352383</td>\n",
       "      <td>-0.137030</td>\n",
       "      <td>176989.076471</td>\n",
       "      <td>29480.108606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-28</th>\n",
       "      <td>59548.007622</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>8.538955</td>\n",
       "      <td>10.994538</td>\n",
       "      <td>11.188692</td>\n",
       "      <td>72308.133440</td>\n",
       "      <td>-0.194154</td>\n",
       "      <td>177172.308036</td>\n",
       "      <td>29510.628492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-29</th>\n",
       "      <td>59100.774755</td>\n",
       "      <td>5111.0</td>\n",
       "      <td>8.539150</td>\n",
       "      <td>10.986999</td>\n",
       "      <td>11.189726</td>\n",
       "      <td>72382.980866</td>\n",
       "      <td>-0.202727</td>\n",
       "      <td>177355.702221</td>\n",
       "      <td>29541.175465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-30</th>\n",
       "      <td>59339.790995</td>\n",
       "      <td>5112.0</td>\n",
       "      <td>8.539346</td>\n",
       "      <td>10.991035</td>\n",
       "      <td>11.190761</td>\n",
       "      <td>72457.894710</td>\n",
       "      <td>-0.199726</td>\n",
       "      <td>177539.259148</td>\n",
       "      <td>29571.749545</td>\n",
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       "    <tr>\n",
       "      <th>2024-08-31</th>\n",
       "      <td>59142.939964</td>\n",
       "      <td>5113.0</td>\n",
       "      <td>8.539542</td>\n",
       "      <td>10.987713</td>\n",
       "      <td>11.191795</td>\n",
       "      <td>72532.875023</td>\n",
       "      <td>-0.204083</td>\n",
       "      <td>177722.978938</td>\n",
       "      <td>29602.350752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4748 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Close    Days  log_days  log_close  log_fitted  \\\n",
       "time                                                                \n",
       "2011-09-02      8.234282   366.0  5.902633   2.108306    1.290595   \n",
       "2011-09-03      8.621424   367.0  5.905362   2.154250    1.297158   \n",
       "2011-09-04      8.449653   368.0  5.908083   2.134125    1.303710   \n",
       "2011-09-05      8.203471   369.0  5.910797   2.104557    1.310251   \n",
       "2011-09-06      7.597333   370.0  5.913503   2.027797    1.316781   \n",
       "...                  ...     ...       ...        ...         ...   \n",
       "2024-08-27  62983.442773  5109.0  8.538759  11.050627   11.187657   \n",
       "2024-08-28  59548.007622  5110.0  8.538955  10.994538   11.188692   \n",
       "2024-08-29  59100.774755  5111.0  8.539150  10.986999   11.189726   \n",
       "2024-08-30  59339.790995  5112.0  8.539346  10.991035   11.190761   \n",
       "2024-08-31  59142.939964  5113.0  8.539542  10.987713   11.191795   \n",
       "\n",
       "                  fitted  residuals    upper_bound   lower_bound  \n",
       "time                                                              \n",
       "2011-09-02      3.634949   0.817711       8.906500      1.483507  \n",
       "2011-09-03      3.658885   0.857092       8.965147      1.493276  \n",
       "2011-09-04      3.682936   0.830415       9.024079      1.503092  \n",
       "2011-09-05      3.707104   0.794306       9.083296      1.512955  \n",
       "2011-09-06      3.731389   0.711017       9.142800      1.522866  \n",
       "...                  ...        ...            ...           ...  \n",
       "2024-08-27  72233.352383  -0.137030  176989.076471  29480.108606  \n",
       "2024-08-28  72308.133440  -0.194154  177172.308036  29510.628492  \n",
       "2024-08-29  72382.980866  -0.202727  177355.702221  29541.175465  \n",
       "2024-08-30  72457.894710  -0.199726  177539.259148  29571.749545  \n",
       "2024-08-31  72532.875023  -0.204083  177722.978938  29602.350752  \n",
       "\n",
       "[4748 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate residuals\n",
    "df['residuals'] = df['log_close'] - df['log_fitted']\n",
    "\n",
    "# Shift residuals to make them all positive for log-normal fitting\n",
    "shifted_residuals = df['residuals'] - df['residuals'].min() + 1e-9  # Shift by the minimum value plus a small epsilon\n",
    "\n",
    "# Fit to a log-normal distribution\n",
    "shape, loc, scale = lognorm.fit(shifted_residuals, floc=0)\n",
    "\n",
    "# Calculate deviation levels with adjusted scaling\n",
    "deviation_scale = 1.5  # Adjust this scale to control how wide the bounds are\n",
    "\n",
    "df['upper_bound'] = np.exp(df['log_fitted'] + deviation_scale * lognorm.ppf(0.34, shape, loc=loc, scale=scale))\n",
    "df['lower_bound'] = np.exp(df['log_fitted'] - deviation_scale * lognorm.ppf(0.34, shape, loc=loc, scale=scale))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e48a70b-b8c3-4a7c-8c65-7907eadb94b1",
   "metadata": {},
   "source": [
    "## Create future dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d29dbad-634d-4dfd-aca5-671b7fcc6ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Project the future (let's assume 365 days into the future)\n",
    "future_days = np.arange(df['Days'].max() + 1, df['Days'].max() + 366)\n",
    "future_log_days = np.log(future_days)\n",
    "future_log_fitted = ransac.predict(future_log_days.reshape(-1, 1))\n",
    "future_fitted = np.exp(future_log_fitted)\n",
    "\n",
    "# Generate future dates starting from one day after the last available date\n",
    "future_dates = pd.date_range(start=df.index[-1] + pd.Timedelta(days=1), periods=365, freq='D')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2b34eb-1ace-4036-a9a3-dd11e300211d",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03b969a5-70e7-4ca1-bb28-7f22cc70fef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/126849320.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['upper_bound'] = np.exp(df['log_fitted'] + deviation_scale * lognorm.ppf(0.5, shape, loc=loc, scale=scale))\n",
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/126849320.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['lower_bound'] = np.exp(df['log_fitted'] - deviation_scale * lognorm.ppf(0.5, shape, loc=loc, scale=scale))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value of the fit: 0.9387\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from scipy.stats import lognorm\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Assume df and model have been defined and calculated in previous parts\n",
    "# Define the number of days into the future for projections\n",
    "future_days = 500\n",
    "\n",
    "# Create a range of future dates\n",
    "last_date = df.index[-1]\n",
    "future_dates = pd.date_range(start=last_date, periods=future_days + 1, freq='D')[1:]\n",
    "\n",
    "# Create future projections DataFrame\n",
    "future_df = pd.DataFrame(index=future_dates)\n",
    "future_df['Days'] = np.arange(df['Days'].max() + 1, df['Days'].max() + 1 + future_days)\n",
    "future_df['log_days'] = np.log(future_df['Days'])\n",
    "\n",
    "# Compute future fitted values using the RANSAC model\n",
    "future_df['log_fitted'] = ransac.predict(future_df['log_days'].values.reshape(-1, 1))\n",
    "future_df['fitted'] = np.exp(future_df['log_fitted'])\n",
    "\n",
    "# Assuming deviation_scale, shape, loc, scale are already defined for bounds calculation\n",
    "future_df['upper_bound'] = np.exp(future_df['log_fitted'] + deviation_scale * lognorm.ppf(0.5, shape, loc=loc, scale=scale))\n",
    "future_df['lower_bound'] = np.exp(future_df['log_fitted'] - deviation_scale * lognorm.ppf(0.5, shape, loc=loc, scale=scale))\n",
    "\n",
    "# Also calculate the bounds for the historical data\n",
    "df['upper_bound'] = np.exp(df['log_fitted'] + deviation_scale * lognorm.ppf(0.5, shape, loc=loc, scale=scale))\n",
    "df['lower_bound'] = np.exp(df['log_fitted'] - deviation_scale * lognorm.ppf(0.5, shape, loc=loc, scale=scale))\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.plot(df.index, df['Close'], label='BTC-USD Close', color='blue')\n",
    "plt.plot(df.index, df['fitted'], label='Power Law Fit', color='red')\n",
    "plt.plot(future_df.index, future_df['fitted'], label='Future Projection', color='orange')\n",
    "\n",
    "# Plot upper and lower bounds\n",
    "plt.fill_between(df.index, df['lower_bound'], df['upper_bound'], color='gray', alpha=0.3, label='Historical Boundaries')\n",
    "plt.fill_between(future_df.index, future_df['lower_bound'], future_df['upper_bound'], color='orange', alpha=0.2, label='Future Boundaries')\n",
    "\n",
    "# Add gridlines for years and quarters\n",
    "ax = plt.gca()  # Get current axis\n",
    "ax.xaxis.set_major_locator(mdates.YearLocator())  # Set major ticks to January 1st of each year\n",
    "ax.xaxis.set_minor_locator(mdates.MonthLocator(bymonth=(4, 7, 10)))  # Set minor ticks to the start of each quarter\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format major ticks as full year\n",
    "\n",
    "plt.legend()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('BTC-USD Price (Log Scale)')\n",
    "plt.title('BTC Power Law Model with Deviation Levels')\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "plt.show()\n",
    "\n",
    "# Output R-squared value\n",
    "r_value = r2_score(df['log_close'], df['log_fitted'])\n",
    "print(f\"R-squared value of the fit: {r_value:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5bc14428-3b3c-47e0-a23e-eff887dbe072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days</th>\n",
       "      <th>log_days</th>\n",
       "      <th>log_fitted</th>\n",
       "      <th>fitted</th>\n",
       "      <th>upper_bound</th>\n",
       "      <th>lower_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-12-15</th>\n",
       "      <td>5584.0</td>\n",
       "      <td>8.627661</td>\n",
       "      <td>11.662826</td>\n",
       "      <td>116171.926161</td>\n",
       "      <td>417193.438634</td>\n",
       "      <td>32349.301734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-16</th>\n",
       "      <td>5585.0</td>\n",
       "      <td>8.627840</td>\n",
       "      <td>11.663794</td>\n",
       "      <td>116284.381942</td>\n",
       "      <td>417597.286755</td>\n",
       "      <td>32380.616236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-17</th>\n",
       "      <td>5586.0</td>\n",
       "      <td>8.628019</td>\n",
       "      <td>11.664761</td>\n",
       "      <td>116396.931325</td>\n",
       "      <td>418001.471017</td>\n",
       "      <td>32411.956802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-18</th>\n",
       "      <td>5587.0</td>\n",
       "      <td>8.628198</td>\n",
       "      <td>11.665729</td>\n",
       "      <td>116509.574377</td>\n",
       "      <td>418405.991658</td>\n",
       "      <td>32443.323452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-19</th>\n",
       "      <td>5588.0</td>\n",
       "      <td>8.628377</td>\n",
       "      <td>11.666696</td>\n",
       "      <td>116622.311162</td>\n",
       "      <td>418810.848912</td>\n",
       "      <td>32474.716202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-20</th>\n",
       "      <td>5589.0</td>\n",
       "      <td>8.628556</td>\n",
       "      <td>11.667663</td>\n",
       "      <td>116735.141747</td>\n",
       "      <td>419216.043018</td>\n",
       "      <td>32506.135072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-21</th>\n",
       "      <td>5590.0</td>\n",
       "      <td>8.628735</td>\n",
       "      <td>11.668630</td>\n",
       "      <td>116848.066197</td>\n",
       "      <td>419621.574211</td>\n",
       "      <td>32537.580080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-22</th>\n",
       "      <td>5591.0</td>\n",
       "      <td>8.628913</td>\n",
       "      <td>11.669597</td>\n",
       "      <td>116961.084579</td>\n",
       "      <td>420027.442728</td>\n",
       "      <td>32569.051244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-23</th>\n",
       "      <td>5592.0</td>\n",
       "      <td>8.629092</td>\n",
       "      <td>11.670563</td>\n",
       "      <td>117074.196959</td>\n",
       "      <td>420433.648805</td>\n",
       "      <td>32600.548582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-24</th>\n",
       "      <td>5593.0</td>\n",
       "      <td>8.629271</td>\n",
       "      <td>11.671530</td>\n",
       "      <td>117187.403401</td>\n",
       "      <td>420840.192681</td>\n",
       "      <td>32632.072114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-25</th>\n",
       "      <td>5594.0</td>\n",
       "      <td>8.629450</td>\n",
       "      <td>11.672496</td>\n",
       "      <td>117300.703974</td>\n",
       "      <td>421247.074592</td>\n",
       "      <td>32663.621857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-26</th>\n",
       "      <td>5595.0</td>\n",
       "      <td>8.629629</td>\n",
       "      <td>11.673462</td>\n",
       "      <td>117414.098741</td>\n",
       "      <td>421654.294775</td>\n",
       "      <td>32695.197829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-27</th>\n",
       "      <td>5596.0</td>\n",
       "      <td>8.629807</td>\n",
       "      <td>11.674428</td>\n",
       "      <td>117527.587771</td>\n",
       "      <td>422061.853468</td>\n",
       "      <td>32726.800050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-28</th>\n",
       "      <td>5597.0</td>\n",
       "      <td>8.629986</td>\n",
       "      <td>11.675394</td>\n",
       "      <td>117641.171128</td>\n",
       "      <td>422469.750909</td>\n",
       "      <td>32758.428537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-29</th>\n",
       "      <td>5598.0</td>\n",
       "      <td>8.630165</td>\n",
       "      <td>11.676360</td>\n",
       "      <td>117754.848879</td>\n",
       "      <td>422877.987335</td>\n",
       "      <td>32790.083310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-30</th>\n",
       "      <td>5599.0</td>\n",
       "      <td>8.630343</td>\n",
       "      <td>11.677326</td>\n",
       "      <td>117868.621090</td>\n",
       "      <td>423286.562983</td>\n",
       "      <td>32821.764385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12-31</th>\n",
       "      <td>5600.0</td>\n",
       "      <td>8.630522</td>\n",
       "      <td>11.678291</td>\n",
       "      <td>117982.487828</td>\n",
       "      <td>423695.478093</td>\n",
       "      <td>32853.471783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-01</th>\n",
       "      <td>5601.0</td>\n",
       "      <td>8.630700</td>\n",
       "      <td>11.679257</td>\n",
       "      <td>118096.449159</td>\n",
       "      <td>424104.732901</td>\n",
       "      <td>32885.205521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-02</th>\n",
       "      <td>5602.0</td>\n",
       "      <td>8.630879</td>\n",
       "      <td>11.680222</td>\n",
       "      <td>118210.505149</td>\n",
       "      <td>424514.327647</td>\n",
       "      <td>32916.965618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-03</th>\n",
       "      <td>5603.0</td>\n",
       "      <td>8.631057</td>\n",
       "      <td>11.681187</td>\n",
       "      <td>118324.655864</td>\n",
       "      <td>424924.262569</td>\n",
       "      <td>32948.752092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-04</th>\n",
       "      <td>5604.0</td>\n",
       "      <td>8.631236</td>\n",
       "      <td>11.682153</td>\n",
       "      <td>118438.901371</td>\n",
       "      <td>425334.537905</td>\n",
       "      <td>32980.564962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-05</th>\n",
       "      <td>5605.0</td>\n",
       "      <td>8.631414</td>\n",
       "      <td>11.683117</td>\n",
       "      <td>118553.241737</td>\n",
       "      <td>425745.153893</td>\n",
       "      <td>33012.404246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-06</th>\n",
       "      <td>5606.0</td>\n",
       "      <td>8.631593</td>\n",
       "      <td>11.684082</td>\n",
       "      <td>118667.677027</td>\n",
       "      <td>426156.110774</td>\n",
       "      <td>33044.269964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-07</th>\n",
       "      <td>5607.0</td>\n",
       "      <td>8.631771</td>\n",
       "      <td>11.685047</td>\n",
       "      <td>118782.207309</td>\n",
       "      <td>426567.408785</td>\n",
       "      <td>33076.162132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-08</th>\n",
       "      <td>5608.0</td>\n",
       "      <td>8.631949</td>\n",
       "      <td>11.686011</td>\n",
       "      <td>118896.832649</td>\n",
       "      <td>426979.048166</td>\n",
       "      <td>33108.080771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-09</th>\n",
       "      <td>5609.0</td>\n",
       "      <td>8.632128</td>\n",
       "      <td>11.686976</td>\n",
       "      <td>119011.553114</td>\n",
       "      <td>427391.029156</td>\n",
       "      <td>33140.025898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-10</th>\n",
       "      <td>5610.0</td>\n",
       "      <td>8.632306</td>\n",
       "      <td>11.687940</td>\n",
       "      <td>119126.368770</td>\n",
       "      <td>427803.351994</td>\n",
       "      <td>33171.997532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-11</th>\n",
       "      <td>5611.0</td>\n",
       "      <td>8.632484</td>\n",
       "      <td>11.688904</td>\n",
       "      <td>119241.279684</td>\n",
       "      <td>428216.016920</td>\n",
       "      <td>33203.995691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-12</th>\n",
       "      <td>5612.0</td>\n",
       "      <td>8.632662</td>\n",
       "      <td>11.689868</td>\n",
       "      <td>119356.285923</td>\n",
       "      <td>428629.024174</td>\n",
       "      <td>33236.020395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01-13</th>\n",
       "      <td>5613.0</td>\n",
       "      <td>8.632841</td>\n",
       "      <td>11.690832</td>\n",
       "      <td>119471.387553</td>\n",
       "      <td>429042.373994</td>\n",
       "      <td>33268.071661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Days  log_days  log_fitted         fitted    upper_bound  \\\n",
       "2025-12-15  5584.0  8.627661   11.662826  116171.926161  417193.438634   \n",
       "2025-12-16  5585.0  8.627840   11.663794  116284.381942  417597.286755   \n",
       "2025-12-17  5586.0  8.628019   11.664761  116396.931325  418001.471017   \n",
       "2025-12-18  5587.0  8.628198   11.665729  116509.574377  418405.991658   \n",
       "2025-12-19  5588.0  8.628377   11.666696  116622.311162  418810.848912   \n",
       "2025-12-20  5589.0  8.628556   11.667663  116735.141747  419216.043018   \n",
       "2025-12-21  5590.0  8.628735   11.668630  116848.066197  419621.574211   \n",
       "2025-12-22  5591.0  8.628913   11.669597  116961.084579  420027.442728   \n",
       "2025-12-23  5592.0  8.629092   11.670563  117074.196959  420433.648805   \n",
       "2025-12-24  5593.0  8.629271   11.671530  117187.403401  420840.192681   \n",
       "2025-12-25  5594.0  8.629450   11.672496  117300.703974  421247.074592   \n",
       "2025-12-26  5595.0  8.629629   11.673462  117414.098741  421654.294775   \n",
       "2025-12-27  5596.0  8.629807   11.674428  117527.587771  422061.853468   \n",
       "2025-12-28  5597.0  8.629986   11.675394  117641.171128  422469.750909   \n",
       "2025-12-29  5598.0  8.630165   11.676360  117754.848879  422877.987335   \n",
       "2025-12-30  5599.0  8.630343   11.677326  117868.621090  423286.562983   \n",
       "2025-12-31  5600.0  8.630522   11.678291  117982.487828  423695.478093   \n",
       "2026-01-01  5601.0  8.630700   11.679257  118096.449159  424104.732901   \n",
       "2026-01-02  5602.0  8.630879   11.680222  118210.505149  424514.327647   \n",
       "2026-01-03  5603.0  8.631057   11.681187  118324.655864  424924.262569   \n",
       "2026-01-04  5604.0  8.631236   11.682153  118438.901371  425334.537905   \n",
       "2026-01-05  5605.0  8.631414   11.683117  118553.241737  425745.153893   \n",
       "2026-01-06  5606.0  8.631593   11.684082  118667.677027  426156.110774   \n",
       "2026-01-07  5607.0  8.631771   11.685047  118782.207309  426567.408785   \n",
       "2026-01-08  5608.0  8.631949   11.686011  118896.832649  426979.048166   \n",
       "2026-01-09  5609.0  8.632128   11.686976  119011.553114  427391.029156   \n",
       "2026-01-10  5610.0  8.632306   11.687940  119126.368770  427803.351994   \n",
       "2026-01-11  5611.0  8.632484   11.688904  119241.279684  428216.016920   \n",
       "2026-01-12  5612.0  8.632662   11.689868  119356.285923  428629.024174   \n",
       "2026-01-13  5613.0  8.632841   11.690832  119471.387553  429042.373994   \n",
       "\n",
       "             lower_bound  \n",
       "2025-12-15  32349.301734  \n",
       "2025-12-16  32380.616236  \n",
       "2025-12-17  32411.956802  \n",
       "2025-12-18  32443.323452  \n",
       "2025-12-19  32474.716202  \n",
       "2025-12-20  32506.135072  \n",
       "2025-12-21  32537.580080  \n",
       "2025-12-22  32569.051244  \n",
       "2025-12-23  32600.548582  \n",
       "2025-12-24  32632.072114  \n",
       "2025-12-25  32663.621857  \n",
       "2025-12-26  32695.197829  \n",
       "2025-12-27  32726.800050  \n",
       "2025-12-28  32758.428537  \n",
       "2025-12-29  32790.083310  \n",
       "2025-12-30  32821.764385  \n",
       "2025-12-31  32853.471783  \n",
       "2026-01-01  32885.205521  \n",
       "2026-01-02  32916.965618  \n",
       "2026-01-03  32948.752092  \n",
       "2026-01-04  32980.564962  \n",
       "2026-01-05  33012.404246  \n",
       "2026-01-06  33044.269964  \n",
       "2026-01-07  33076.162132  \n",
       "2026-01-08  33108.080771  \n",
       "2026-01-09  33140.025898  \n",
       "2026-01-10  33171.997532  \n",
       "2026-01-11  33203.995691  \n",
       "2026-01-12  33236.020395  \n",
       "2026-01-13  33268.071661  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_df.tail(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9daa5a-f92d-4f75-a884-d07203b2eec1",
   "metadata": {},
   "source": [
    "## % Integral and Maximum Change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b005eec-8c3a-427c-9d21-7f49e5886242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Section from 2013-07-11 00:00:00 to 2014-12-16 00:00:00: Integral of relative % change = 93355.0923, Maximum % change = 855.02%\n",
      "Section from 2017-07-21 00:00:00 to 2018-11-19 00:00:00: Integral of relative % change = 58902.5657, Maximum % change = 549.99%\n",
      "Section from 2020-11-06 00:00:00 to 2022-05-18 00:00:00: Integral of relative % change = 61247.0864, Maximum % change = 261.06%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/41381015.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.loc[:, 'relative_pct_change'] = (df['Close'] - df['fitted']) / df['fitted'] * 100\n",
      "/var/folders/b8/z397yjvn3xj72ht4tz6zzjs00000gn/T/ipykernel_71200/41381015.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.loc[:, 'above_fitted'] = df['relative_pct_change'] > 0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.integrate import simpson  # For numerical integration\n",
    "\n",
    "# 1. Calculate the relative % change of the BTC close price from the fitted line\n",
    "df.loc[:, 'relative_pct_change'] = (df['Close'] - df['fitted']) / df['fitted'] * 100\n",
    "\n",
    "# 2. Identify sections where the BTC close price is greater than the fitted line for at least 3 weeks straight\n",
    "# We need to identify consecutive periods where the relative_pct_change is positive for at least 21 days (3 weeks)\n",
    "df.loc[:, 'above_fitted'] = df['relative_pct_change'] > 0\n",
    "\n",
    "# Find the start and end indices of these periods\n",
    "sections = []\n",
    "start_idx = None\n",
    "\n",
    "for i in range(len(df)):\n",
    "    if df['above_fitted'].iloc[i]:\n",
    "        if start_idx is None:\n",
    "            start_idx = i\n",
    "    else:\n",
    "        if start_idx is not None:\n",
    "            if i - start_idx >= 21:  # Ensure it's at least 21 days (3 weeks)\n",
    "                sections.append((start_idx, i - 1))\n",
    "            start_idx = None\n",
    "\n",
    "# In case the last segment goes to the end of the dataframe\n",
    "if start_idx is not None and len(df) - start_idx >= 21:\n",
    "    sections.append((start_idx, len(df) - 1))\n",
    "\n",
    "# 3. Calculate the integral of the relative % change and the maximum % change for each identified section\n",
    "results = []\n",
    "\n",
    "for start, end in sections:\n",
    "    time_points = np.arange(end - start + 1)  # Create an array of time points for integration\n",
    "    values = df['relative_pct_change'].iloc[start:end + 1].values\n",
    "    integral = simpson(values, x=time_points)  # Explicitly specify the x argument\n",
    "    \n",
    "    # Only keep sections where the integral is greater than 10,000%\n",
    "    if integral >= 10000:\n",
    "        max_pct_change = values.max()  # Calculate the maximum % change for the section\n",
    "        results.append((start, end, integral, max_pct_change))\n",
    "\n",
    "# Display the results\n",
    "for start, end, integral, max_pct_change in results:\n",
    "    print(f\"Section from {df.index[start]} to {df.index[end]}: Integral of relative % change = {integral:.4f}, Maximum % change = {max_pct_change:.2f}%\")"
   ]
  }
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