{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Top 10 Cryptos Price Predictions and Forecasts\n", "\n", "# TABLE OF CONTENTS\n", "\n", "1. [DATA PREPARATION](#prepare)\n", " 1. [Cryptocurrency Market Data](#cmd)\n", " 1. [Missing Data](#missing)\n", " 2. [Explore Daily Price Trends](#edpt)\n", " 3. [Explore Top 10 Cryptos Popularity](#popularity)\n", "2. [FIND CORRELATIONS](#find_corr)\n", " 1. [Correlations Plot](#corr_plots)\n", "3. [BUILDING MODEL](#building_models)\n", " 1. [Feature Selection](#feature_select)\n", " 2. [Handling Missing Values](#missing_data)\n", " 3. [Train-Test Split](#train_test)\n", " 4. [Model Selection](#model_select)\n", " 5. [Model Evaluation](#model_eval)\n", "4. [RESULT](#results)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import json\n", "import time\n", "import requests\n", "import seaborn as sns\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# DATA PREPARATION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cryptocurrency Market Data" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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openhighlowclosevolumeweightedAveragedateidsymbol
027210.3627350.0026605.0526817.9339705.7602027014.1452023-06-01215BTC
126817.9327300.0026505.0027242.5936765.3019127030.2602023-06-02215BTC
227242.5927333.2926914.9327069.2216326.7850427155.9052023-06-03215BTC
327069.2227455.0226951.0027115.2018888.1901627092.2102023-06-04215BTC
427115.1427129.3325388.0025728.2069948.6554326421.6702023-06-05215BTC
\n", "
" ], "text/plain": [ " open high low close volume weightedAverage \\\n", "0 27210.36 27350.00 26605.05 26817.93 39705.76020 27014.145 \n", "1 26817.93 27300.00 26505.00 27242.59 36765.30191 27030.260 \n", "2 27242.59 27333.29 26914.93 27069.22 16326.78504 27155.905 \n", "3 27069.22 27455.02 26951.00 27115.20 18888.19016 27092.210 \n", "4 27115.14 27129.33 25388.00 25728.20 69948.65543 26421.670 \n", "\n", " date id symbol \n", "0 2023-06-01 215 BTC \n", "1 2023-06-02 215 BTC \n", "2 2023-06-03 215 BTC \n", "3 2023-06-04 215 BTC \n", "4 2023-06-05 215 BTC " ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ohlcv_data = pd.read_csv('data/cryptos-ohlcv-daily.csv')\n", "ohlcv_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "open 0\n", "high 0\n", "low 0\n", "close 0\n", "volume 0\n", "weightedAverage 0\n", "date 0\n", "id 0\n", "symbol 0\n", "dtype: int64\n", "open 0\n", "high 0\n", "low 0\n", "close 0\n", "volume 0\n", "weightedAverage 0\n", "date 0\n", "id 0\n", "symbol 0\n", "dtype: int64\n" ] } ], "source": [ "ohlcv_data = ohlcv_data.dropna() \n", "print(ohlcv_data.isnull().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore Daily Price Trends" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "close": [ 26817.93, 27242.59, 27069.22, 27115.2, 25728.2, 27230.08, 26339.34, 26477.81, 25841.21, 25925.54, 25905.2, 25932.72, 25128.6, 25598.49, 26345.01, 26516.99, 26339.97, 26844.36, 28307.99, 29993.89, 29884.92, 30688.5, 30527.43, 30462.66, 30267.99, 30692.45, 30077.4, 30447.31, 30472, 30585.9, 30617.02, 31156.2, 30766.52, 30504.81, 29895.43, 30344.7, 30284.63, 30160.71, 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"2023-06-01T00:00:00", "2023-07-01T00:00:00", "2023-08-01T00:00:00", "2023-09-01T00:00:00", "2023-10-01T00:00:00", "2023-11-01T00:00:00", "2023-12-01T00:00:00", "2024-01-01T00:00:00", "2024-02-01T00:00:00", "2024-03-01T00:00:00", "2024-04-01T00:00:00", "2024-05-01T00:00:00", "2024-06-01T00:00:00", "2024-07-01T00:00:00", "2024-08-01T00:00:00", "2024-09-01T00:00:00", "2024-10-01T00:00:00", "2024-11-01T00:00:00", "2024-12-01T00:00:00" ], "title": { "text": "Date" } }, "yaxis": { "title": { "text": "Price (USD)" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Convert 'date' column to datetime for better plotting\n", "symbols = ohlcv_data['symbol'].unique()\n", "\n", "# Loop through each symbol and create a candlestick plot\n", "for symbol in symbols:\n", " # Filter data for the current symbol\n", " symbol_data = ohlcv_data[ohlcv_data['symbol'] == symbol]\n", " \n", " # Create the candlestick plot\n", " fig = go.Figure(data=[go.Candlestick(\n", " x=symbol_data['date'],\n", " open=symbol_data['open'],\n", " high=symbol_data['high'],\n", " low=symbol_data['low'],\n", " close=symbol_data['close']\n", " )])\n", " \n", " # Update the layout for better visualization\n", " fig.update_layout(\n", " title=f'{symbol} Daily Candlestick Chart',\n", " xaxis_title='Date',\n", " yaxis_title='Price (USD)',\n", " xaxis_rangeslider_visible=False, # Optional: Hide range slider\n", " template=\"plotly_white\",\n", " xaxis=dict(\n", " tickformat='%b %Y', # Show month and year\n", " tickvals=pd.date_range(start=symbol_data['date'].min(), end=symbol_data['date'].max(), freq='MS'), # Monthly ticks\n", " tickangle=-45\n", " ),\n", " )\n", " \n", " # Show the plot for the current symbol\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore Top 10 Cryptos Popularity" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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symbolweighted_score
0BTC3.466140
553ETH1.170499
1106BNB1.027916
1659SOL1.001880
2212XRP1.000041
2765TRX1.000002
3318AVAX1.001281
3871DOGE1.000001
4424DOT1.000475
4977LTC1.008421
\n", "
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"white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "size": 18 }, "text": "Popular Cryptocurrency Weight Distribution", "x": 0.5 }, "width": 800, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "tickangle": -45, "title": { "text": "Cryptocurrency" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Weight" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_min = ohlcv_data['weightedAverage'].min()\n", "global_max = ohlcv_data['weightedAverage'].max()\n", "\n", "ohlcv_data['weighted_score'] = ohlcv_data['weightedAverage'].apply(\n", " lambda x: 1 + (x - global_min) / (global_max - global_min) * 9\n", ")\n", "\n", "# Create a new DataFrame with unique symbols and their normalized weighted scores\n", "unique_crypto_weights = ohlcv_data[['symbol', 'weighted_score']].drop_duplicates(subset='symbol')\n", "\n", "display(unique_crypto_weights)\n", "\n", "fig = px.bar(\n", " unique_crypto_weights,\n", " x='symbol',\n", " y='weighted_score',\n", " text='weighted_score',\n", " color='weighted_score',\n", " color_continuous_scale=px.colors.sequential.YlOrBr,\n", " title='Popular Cryptocurrency Weight Distribution',\n", " labels={'symbol': 'Cryptocurrency', 'weighted_score': 'Weight'}\n", ")\n", "\n", "fig.update_traces(\n", " texttemplate='%{text:.2f}', # Show text inside the bars\n", " textposition='inside'\n", ")\n", "fig.update_layout(\n", " width=800, \n", " height=600,\n", " xaxis=dict(title='Cryptocurrency', tickangle=-45), \n", " yaxis=dict(title='Weight'),\n", " coloraxis_colorbar=dict(title=\"Weight\"), \n", " title=dict(font=dict(size=18), x=0.5), \n", " plot_bgcolor='rgba(240,240,240,0.5)' \n", ")\n", "\n", "# Show the plot\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FIND CORRELATIONS" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " open high low close volume\n", "open 1.000000 0.999775 0.999777 0.999597 -0.104230\n", "high 0.999775 1.000000 0.999699 0.999862 -0.104174\n", "low 0.999777 0.999699 1.000000 0.999797 -0.104254\n", "close 0.999597 0.999862 0.999797 1.000000 -0.104201\n", "volume -0.104230 -0.104174 -0.104254 -0.104201 1.000000\n" ] } ], "source": [ "numerical_columns = ['open', 'high', 'low', 'close', 'volume']\n", "numerical_data = ohlcv_data[numerical_columns]\n", "correlation_matrix = numerical_data.corr()\n", "print(correlation_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlations Plot" ] }, { "cell_type": "code", "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the heatmap\n", "plt.figure(figsize=(10, 8))\n", "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n", "plt.title('Correlation Matrix for Cryptocurrency Data', fontsize=16)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BUILDING MODEL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Selection" ] }, { "cell_type": "code", "execution_count": 178, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " open high low close volume weightedAverage \\\n", "19 28308.00 30800.00 28257.99 29993.89 1.142210e+05 29150.945 \n", "20 29993.89 30500.00 29525.61 29884.92 6.331282e+04 29939.405 \n", "21 29884.91 31431.94 29800.00 30688.50 8.158243e+04 30286.705 \n", "22 30688.51 30800.00 30250.00 30527.43 3.201427e+04 30607.970 \n", "23 30527.44 31046.01 30277.49 30462.66 3.155572e+04 30495.050 \n", "... ... ... ... ... ... ... \n", "5525 102.75 122.25 99.35 119.57 1.807382e+06 111.160 \n", "5526 119.57 139.42 114.92 132.93 4.045921e+06 126.250 \n", "5527 132.75 135.16 122.70 130.89 1.956176e+06 131.820 \n", "5528 130.90 135.62 121.97 133.69 1.384338e+06 132.295 \n", "5529 133.69 146.79 124.94 135.78 2.098452e+06 134.735 \n", "\n", " date id symbol weighted_score ma_5 ma_20 lag_1 \\\n", "19 2023-06-21 215 BTC 3.661211 27600.640 26635.0170 28307.99 \n", "20 2023-06-22 215 BTC 3.733190 28274.226 26788.3665 29993.89 \n", "21 2023-06-23 215 BTC 3.764895 29143.932 26960.6620 29884.92 \n", "22 2023-06-24 215 BTC 3.794224 29880.546 27133.5725 30688.50 \n", "23 2023-06-25 215 BTC 3.783915 30311.480 27300.9455 30527.43 \n", "... ... ... ... ... ... ... ... \n", "5525 2024-12-01 887 LTC 1.010143 103.970 92.1560 102.74 \n", "5526 2024-12-02 887 LTC 1.011520 111.110 94.9680 119.57 \n", "5527 2024-12-03 887 LTC 1.012029 118.180 97.7430 132.93 \n", "5528 2024-12-04 887 LTC 1.012072 123.964 100.3085 130.89 \n", "5529 2024-12-05 887 LTC 1.012295 130.572 102.9125 133.69 \n", "\n", " lag_2 lag_3 daily_return \n", "19 26844.36 26339.97 0.059556 \n", "20 28307.99 26844.36 -0.003633 \n", "21 29993.89 28307.99 0.026889 \n", "22 29884.92 29993.89 -0.005249 \n", "23 30688.50 29884.92 -0.002122 \n", "... ... ... ... \n", "5525 104.77 95.54 0.163812 \n", "5526 102.74 104.77 0.111734 \n", "5527 119.57 102.74 -0.015346 \n", "5528 132.93 119.57 0.021392 \n", "5529 130.89 132.93 0.015633 \n", "\n", "[5340 rows x 16 columns]\n" ] } ], "source": [ "model_data = ohlcv_data\n", "\n", "model_data['ma_5'] = model_data.groupby('symbol')['close'].transform(lambda x: x.rolling(window=5).mean())\n", "model_data['ma_20'] = model_data.groupby('symbol')['close'].transform(lambda x: x.rolling(window=20).mean())\n", "\n", "# Add lagged features\n", "for lag in range(1, 4): # Lagged values for the past 3 days\n", " model_data[f'lag_{lag}'] = model_data.groupby('symbol')['close'].shift(lag)\n", "\n", "# Add daily return as a feature\n", "model_data['daily_return'] = model_data.groupby('symbol')['close'].pct_change()\n", "\n", "model_data = model_data.dropna()\n", "# Columns used to train the model (e.g., open, high, low, volume, etc.).\n", "features = [\n", " 'open', 'high', 'low', 'volume', 'ma_5', 'ma_20', \n", " 'lag_1', 'lag_2', 'lag_3'\n", "]\n", "\n", "print(model_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling Missing Values" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing values in features:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:7: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:7: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:7: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:7: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:7: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "SeriesGroupBy.fillna is deprecated and will be removed in a future version. Use obj.ffill() or obj.bfill() for forward or backward filling instead. If you want to fill with a single value, use Series.fillna instead\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "SeriesGroupBy.fillna is deprecated and will be removed in a future version. Use obj.ffill() or obj.bfill() for forward or backward filling instead. If you want to fill with a single value, use Series.fillna instead\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "SeriesGroupBy.fillna is deprecated and will be removed in a future version. Use obj.ffill() or obj.bfill() for forward or backward filling instead. If you want to fill with a single value, use Series.fillna instead\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "SeriesGroupBy.fillna is deprecated and will be removed in a future version. Use obj.ffill() or obj.bfill() for forward or backward filling instead. If you want to fill with a single value, use Series.fillna instead\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "SeriesGroupBy.fillna is deprecated and will be removed in a future version. Use obj.ffill() or obj.bfill() for forward or backward filling instead. If you want to fill with a single value, use Series.fillna instead\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: FutureWarning:\n", "\n", "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:16: SettingWithCopyWarning:\n", "\n", "\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", "\n", "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/2843346195.py:20: SettingWithCopyWarning:\n", "\n", "\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", "\n" ] } ], "source": [ "# Check for missing values in features\n", "print(\"Missing values in features:\")\n", "timeframes = [5, 30, 90, 180, 365]\n", "\n", "# Fill NaN in target columns with the last known price\n", "for t in timeframes:\n", " model_data[f'target_{t}d'] = model_data.groupby('symbol')['close'].transform(\n", " lambda x: x.shift(-t).ewm(span=t, adjust=False).mean()\n", " )\n", "\n", "# Replace NaN values with the last known price\n", "last_known_prices = model_data.groupby('symbol')['close'].last()\n", "\n", "# Step 2: Forward fill missing values within each symbol group\n", "for t in timeframes:\n", " model_data[f'target_{t}d'] = model_data.groupby('symbol')[f'target_{t}d'].fillna(method='ffill')\n", "\n", "# Step 3: Fill remaining NaN values with the last known price for each symbol\n", "for t in timeframes:\n", " model_data[f'target_{t}d'] = model_data.apply(\n", " lambda row: last_known_prices[row['symbol']] if pd.isna(row[f'target_{t}d']) else row[f'target_{t}d'], axis=1\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train-Test Split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "80-20 split: 80% of the data (292 days) is used for training, and 20% (73 days) for testing.\n", "\n", "The 80-20 split is a standard practice in machine learning and data analysis.\n", "\n", "We used time-based split instead of random selection and it is the best practice for time-series data because:\n", "\n", "- It preserves the sequential nature of the data.\n", "- This simulates a real-world scenario where you train the model on historical data and then evaluate or use it to predict future data." ] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4220, 9)\n", "(1120, 9)\n", "(4220, 5)\n", "(1120, 5)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/96/zkx_7hk579v922k6dk_fc26c0000gn/T/ipykernel_26365/1468742617.py:4: SettingWithCopyWarning:\n", "\n", "\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", "\n" ] } ], "source": [ "# Train-test split\n", "from datetime import datetime, timedelta\n", "\n", "model_data['date'] = pd.to_datetime(model_data['date'])\n", "\n", "start_date = datetime(2023, 6, 1)\n", "end_date = datetime(2024, 12, 5)\n", "total_days = (end_date - start_date).days + 1 # Total number of days including the end date\n", "training_days = int(total_days * 0.8) # Calculate the number of training days (80% of total days)\n", "cutoff_date = pd.to_datetime((start_date + timedelta(days=training_days - 1)))# Calculate the cutoff date\n", "\n", "train_data = model_data[model_data['date'] < cutoff_date] # Contains rows before the cutoff date.\n", "test_data = model_data[model_data['date'] >= cutoff_date] # Contains rows from the cutoff date onward.\n", "\n", "targets = [f'target_{t}d' for t in timeframes] # The prediction targets (target_5d, target_30d, etc.) defined for different timeframes (e.g., 5 days, 1 month, etc.).\n", "\n", "X_train = train_data[features]\n", "X_test = test_data[features]\n", "Y_train = train_data[targets]\n", "Y_test = test_data[targets]\n", "\n", "\n", "print(X_train.shape)\n", "print(X_test.shape)\n", "print(Y_train.shape)\n", "print(Y_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Selection" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "# X_train, X_test: Feature sets.\n", "# Y_train, Y_test: Target values for each timeframe.\n", "#Train Model\n", "from sklearn.ensemble import RandomForestRegressor # A machine learning model used for regression tasks.\n", "\n", "# Train models for each target\n", "models = {}\n", "predictions = {}\n", "\n", "for target in Y_train.columns: # Loops through each target column (target_5d, target_30d, etc.).\n", " # Train the model\n", " model = RandomForestRegressor(n_estimators=100, random_state=42) # Trains a separate Random Forest model for each target timeframe.\n", " model.fit(X_train, Y_train[target])\n", " \n", " # Save the model\n", " models[target] = model # Each trained model is stored in the models dictionary for future use.\n", " \n", " # Make predictions\n", " predictions[target] = model.predict(X_test) # Predictions for the test set (X_test) are stored in the predictions dictionary." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Evaluation" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "target_5d - MSE: 20720289.35\n", "target_30d - MSE: 28110491.94\n", "target_90d - MSE: 20997786.79\n", "target_180d - MSE: 3297805.00\n", "target_365d - MSE: 4080.68\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error\n", "\n", "# Mean Squared Error (MSE): A common metric for evaluating regression models.\n", "# Measures the average squared difference between the actual (y_test) and predicted values (predictions[target]).\n", "# Calculates and prints the MSE for each prediction target (target_5d, target_30d, etc.).\n", "# A lower MSE indicates better model performance.\n", "for target in predictions.keys():\n", " mse = mean_squared_error(Y_test[target], predictions[target])\n", " print(f\"{target} - MSE: {mse:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# RESULTS" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SymbolCurrent Price5D Prediction1M Prediction3M Prediction6M Prediction1Y Prediction
0BTC$96,945.63$96,939.33$90,473.03$77,968.14$70,912.84$67,982.73
1ETH$3,785.20$3,725.93$3,337.72$2,985.61$2,926.36$3,122.91
2BNB$717.10$704.77$645.32$605.68$576.16$583.39
3SOL$235.84$233.65$223.79$191.07$171.69$155.72
4LTC$135.78$128.49$99.34$82.41$77.36$74.38
5AVAX$50.30$49.92$40.31$32.95$31.50$28.39
6DOT$10.37$9.98$7.51$5.88$5.73$5.43
7XRP$2.24$2.29$1.50$0.97$0.78$0.64
8DOGE$0.43$0.42$0.36$0.25$0.19$0.16
9TRX$0.32$0.31$0.22$0.18$0.16$0.14
\n", "
" ], "text/plain": [ " Symbol Current Price 5D Prediction 1M Prediction 3M Prediction \\\n", "0 BTC $96,945.63 $96,939.33 $90,473.03 $77,968.14 \n", "1 ETH $3,785.20 $3,725.93 $3,337.72 $2,985.61 \n", "2 BNB $717.10 $704.77 $645.32 $605.68 \n", "3 SOL $235.84 $233.65 $223.79 $191.07 \n", "4 LTC $135.78 $128.49 $99.34 $82.41 \n", "5 AVAX $50.30 $49.92 $40.31 $32.95 \n", "6 DOT $10.37 $9.98 $7.51 $5.88 \n", "7 XRP $2.24 $2.29 $1.50 $0.97 \n", "8 DOGE $0.43 $0.42 $0.36 $0.25 \n", "9 TRX $0.32 $0.31 $0.22 $0.18 \n", "\n", " 6M Prediction 1Y Prediction \n", "0 $70,912.84 $67,982.73 \n", "1 $2,926.36 $3,122.91 \n", "2 $576.16 $583.39 \n", "3 $171.69 $155.72 \n", "4 $77.36 $74.38 \n", "5 $31.50 $28.39 \n", "6 $5.73 $5.43 \n", "7 $0.78 $0.64 \n", "8 $0.19 $0.16 \n", "9 $0.16 $0.14 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Summarize predictions by symbol\n", "\n", "summary = model_data.groupby('symbol').last().reset_index()\n", "# Select relevant columns for summary\n", "summary = summary[['symbol', 'close', 'target_5d', 'target_30d',\n", " 'target_90d', 'target_180d', 'target_365d']].sort_values(\"close\", ascending= False)\n", "\n", "# Rename columns for clarity\n", "summary.rename(columns={\n", " 'name': 'Name',\n", " 'symbol': 'Symbol',\n", " 'category': 'Crypto Type',\n", " 'description': 'Description',\n", " 'close': 'Current Price',\n", " 'target_5d': '5D Prediction',\n", " 'target_30d': '1M Prediction',\n", " 'target_90d': '3M Prediction',\n", " 'target_180d': '6M Prediction',\n", " 'target_365d': '1Y Prediction',\n", "}, inplace=True)\n", "\n", "# Format prices as currency\n", "summary['Current Price'] = summary['Current Price'].apply(lambda x: f\"${x:,.2f}\")\n", "summary['5D Prediction'] = summary['5D Prediction'].apply(lambda x: f\"${x:,.2f}\")\n", "summary['1M Prediction'] = summary['1M Prediction'].apply(lambda x: f\"${x:,.2f}\")\n", "summary['3M Prediction'] = summary['3M Prediction'].apply(lambda x: f\"${x:,.2f}\")\n", "summary['6M Prediction'] = summary['6M Prediction'].apply(lambda x: f\"${x:,.2f}\")\n", "summary['1Y Prediction'] = summary['1Y Prediction'].apply(lambda x: f\"${x:,.2f}\")\n", "\n", "summary.reset_index(drop=True, inplace=True)\n", "\n", "pd.set_option('display.max_colwidth', 100) \n", "pd.set_option('display.max_rows', None) \n", "pd.set_option('display.max_columns', None) \n", "display(summary)\n" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Define the list of coins\n", "coins = summary['Symbol'].unique() # Adjusted to match \"Name\" column in the table\n", "\n", "# Define the timeframes and labels\n", "timeframes = ['5D Prediction', '1M Prediction', '3M Prediction', '6M Prediction', '1Y Prediction']\n", "labels = ['5D', '1M', '3M', '6M', '1Y']\n", "\n", "# Create a 2x5 grid for the plots\n", "fig, axes = plt.subplots(2, 5, figsize=(20, 10)) # 2 rows, 5 columns\n", "axes = axes.flatten() # Flatten axes array for easy iteration\n", "\n", "# Plot data for each coin\n", "for i, coin in enumerate(coins):\n", " # Extract data for the current coin\n", " coin_data = summary[summary['Symbol'] == coin].iloc[0]\n", " predictions = coin_data[timeframes].values\n", "\n", " # Convert predictions to float for plotting (if they are strings with \"$\" formatting)\n", " predictions = [float(pred.replace('$', '').replace(',', '')) for pred in predictions]\n", "\n", " # Plot the predictions\n", " axes[i].plot(labels, predictions, marker='o', linestyle='-', color='blue')\n", " axes[i].set_title(f'{coin} Predicted Prices', fontsize=10)\n", " axes[i].set_ylabel('Predicted Price', fontsize=8)\n", " axes[i].set_xlabel('Timeframe', fontsize=8)\n", " axes[i].grid(True)\n", "\n", " # Customize y-axis for better readability\n", " axes[i].tick_params(axis='y', labelsize=8)\n", " axes[i].tick_params(axis='x', labelsize=8)\n", "\n", "# Adjust layout and remove empty subplots\n", "for ax in axes[len(coins):]: # Hide any unused subplots\n", " ax.axis('off')\n", "\n", "# Adjust layout for better spacing\n", "plt.tight_layout()\n", "\n", "# Display the plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Top 10 Cryptocurrency Price Predictions Summary Plot\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "# Define the timeframes and labels\n", "timeframes = ['5D Prediction', '1M Prediction', '3M Prediction', '6M Prediction', '1Y Prediction']\n", "labels = ['5D', '1M', '3M', '6M', '1Y']\n", "\n", "# Create the plot\n", "# Create the plot\n", "plt.figure(figsize=(12, 8))\n", "\n", "# Plot data for each coin\n", "for coin in summary['Symbol'].unique():\n", " coin_data = summary[summary['Symbol'] == coin].iloc[0]\n", " predictions = coin_data[timeframes].values\n", " predictions = [float(pred.replace('$', '').replace(',', '')) for pred in predictions]\n", " plt.plot(labels, predictions, marker='o', label=coin)\n", "\n", "# Add title and labels\n", "plt.title(\"Top 10 Cryptocurrency Price Predictions (Log Scale)\", fontsize=14)\n", "plt.xlabel(\"Timeframe\", fontsize=12)\n", "plt.ylabel(\"Predicted Price (USD)\", fontsize=12)\n", "\n", "# Set Y-axis to logarithmic scale\n", "plt.yscale('log')\n", "\n", "# Add a legend\n", "plt.legend(title=\"Cryptocurrency\", fontsize=10, bbox_to_anchor=(1.05, 1), loc='upper left')\n", "\n", "# Customize grid\n", "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", "\n", "# Adjust layout for better spacing\n", "plt.tight_layout()\n", "\n", "# Display the plot\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 2 }