mirror of
https://github.com/harivansh-afk/CryptoCurrencyPredictionLSTM.git
synced 2026-04-15 06:04:40 +00:00
2285 lines
71 KiB
Text
2285 lines
71 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "PLZl5KnqIGQ8"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import sklearn\n",
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"import matplotlib.pyplot as plt\n",
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"import sys \n",
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"import seaborn as sns\n",
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"\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"%matplotlib inline\n",
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"import plotly.graph_objs as go\n",
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"#import plotly.plotly as py\n",
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"import datetime as dt\n",
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"import matplotlib.dates as mdates"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "RN88EZ6yJYDd"
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},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'tensorflow.python'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32mc:\\Users\\rathi\\OneDrive\\Documents\\-\\Coding\\PYTHON\\CRYPTO PREDICTION\\3+Crypto_Price_Prediction.ipynb Cell 3\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/rathi/OneDrive/Documents/-/Coding/PYTHON/CRYPTO%20PREDICTION/3%2BCrypto_Price_Prediction.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodels\u001b[39;00m \u001b[39mimport\u001b[39;00m Sequential\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/rathi/OneDrive/Documents/-/Coding/PYTHON/CRYPTO%20PREDICTION/3%2BCrypto_Price_Prediction.ipynb#W2sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlayers\u001b[39;00m \u001b[39mimport\u001b[39;00m Dense, LSTM, Dropout, GRU\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/rathi/OneDrive/Documents/-/Coding/PYTHON/CRYPTO%20PREDICTION/3%2BCrypto_Price_Prediction.ipynb#W2sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlayers\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\__init__.py:20\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[39m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[39m\"\"\"Implementation of the Keras API, the high-level API of TensorFlow.\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \n\u001b[0;32m 17\u001b[0m \u001b[39mDetailed documentation and user guides are available at\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \u001b[39m[keras.io](https://keras.io).\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m \u001b[39mimport\u001b[39;00m distribute\n\u001b[0;32m 21\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m \u001b[39mimport\u001b[39;00m models\n\u001b[0;32m 22\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mengine\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39minput_layer\u001b[39;00m \u001b[39mimport\u001b[39;00m Input\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\distribute\\__init__.py:18\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[39m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[39m\"\"\"Keras' Distribution Strategy library.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mkeras\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdistribute\u001b[39;00m \u001b[39mimport\u001b[39;00m sidecar_evaluator\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\distribute\\sidecar_evaluator.py:17\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m# Copyright 2020 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[39m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[39m\"\"\"Python module for evaluation loop.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mv2\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtf\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[39m# isort: off\u001b[39;00m\n\u001b[0;32m 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplatform\u001b[39;00m \u001b[39mimport\u001b[39;00m tf_logging \u001b[39mas\u001b[39;00m logging\n",
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"File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\__init__.py:37\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msys\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39m_sys\u001b[39;00m\n\u001b[0;32m 35\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtyping\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39m_typing\u001b[39;00m\n\u001b[1;32m---> 37\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtools\u001b[39;00m \u001b[39mimport\u001b[39;00m module_util \u001b[39mas\u001b[39;00m _module_util\n\u001b[0;32m 38\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutil\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlazy_loader\u001b[39;00m \u001b[39mimport\u001b[39;00m LazyLoader \u001b[39mas\u001b[39;00m _LazyLoader\n\u001b[0;32m 40\u001b[0m \u001b[39m# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.\u001b[39;00m\n",
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"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.python'"
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]
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}
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],
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"source": [
|
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"from keras.models import Sequential\n",
|
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"from keras.layers import Dense, LSTM, Dropout, GRU\n",
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"from keras.layers import *\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
|
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"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
|
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"from sklearn.model_selection import train_test_split\n",
|
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"from keras.callbacks import EarlyStopping\n",
|
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"from tensorflow.keras.optimizers import Adam, SGD"
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]
|
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
|
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"colab": {
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"background_save": true,
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"base_uri": "https://localhost:8080/",
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"height": 38,
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"resources": {
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"http://localhost:8080/nbextensions/google.colab/files.js": {
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"data": "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",
|
|
"headers": [
|
|
[
|
|
"content-type",
|
|
"application/javascript"
|
|
]
|
|
],
|
|
"ok": true,
|
|
"status": 200,
|
|
"status_text": ""
|
|
}
|
|
}
|
|
},
|
|
"id": "sOGCxI5MIHJ8",
|
|
"outputId": "2a391b7b-7b06-4c09-daaa-dfe79a178c84"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# from google.colab import files\n",
|
|
"# uploaded = files.upload()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "PLXtatQdIKkr"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# import io\n",
|
|
"# df = pd.read_csv(io.StringIO(uploaded['crypto-markets.csv'].decode('utf-8')))\n",
|
|
"\n",
|
|
"df = pd.read_csv(\"crypto-markets.csv\")\n",
|
|
"\n",
|
|
"data_copy = df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 206
|
|
},
|
|
"id": "vFFnVybLILE8",
|
|
"outputId": "5bf743d9-a5cb-4184-dd52-0a346bde79f2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "TDuDU-SlIW8s",
|
|
"outputId": "b5dab737-bce6-4822-c2b5-053c6bc68a26"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.shape #total number of rows and columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "rSQy0La6Iaez",
|
|
"outputId": "efe86655-27e4-4773-a87a-523435d68318"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.isnull().sum() #total of null values column wise "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 300
|
|
},
|
|
"id": "HzaCeYB_Ia_j",
|
|
"outputId": "90fe7dea-0df1-48e2-eb85-e2417ff927d9"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.describe() #statistical readings of all the columns "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "t2R1q-FAIejT",
|
|
"outputId": "aebb2a25-de90-4d15-cfe2-011325915a20"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.info() #info on all the columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "xdF9qRVBM82y"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = df #copy of dataframe\n",
|
|
"\n",
|
|
"#set up a column with an index so I can only sample 1/10th of the dataset \n",
|
|
"data[\"row_id\"]= range(1, len(data) + 1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 206
|
|
},
|
|
"id": "kEwAdAFzNGfa",
|
|
"outputId": "47586bab-8683-4edf-8f21-475aed383a5a"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#drop columns I'm not using\n",
|
|
"data_=data.drop(['slug','ranknow','volume','market'], axis=1)\n",
|
|
"data_.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 238
|
|
},
|
|
"id": "txJAiKwmNNg7",
|
|
"outputId": "6c2070a1-7b4d-4155-f209-0eda8ff7bc41"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_.set_index('row_id', inplace=True)\n",
|
|
"data_.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "Bynw95JgNUx6",
|
|
"outputId": "5e4c28c2-3a39-4a64-8106-c00c1d643c9b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#counitng unique symbols \n",
|
|
"data[\"name\"].unique() #total number of unique cryptos "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "xjq9b8pRNR9C"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#set date to timestamp format\n",
|
|
"data_['date'] = pd.to_datetime(data_['date']).dt.date "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "OVpiyV4PS4wY"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#pick out the currency for two years span (2016>)\n",
|
|
"\n",
|
|
"date = data_[data_['date'] >= dt.date(2016, 1, 1)]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "Vvz_kNbgS5dQ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#show if each one closed up or down each day\n",
|
|
"date['pos_neg']= date['open']-date['close']\n",
|
|
"date.head()\n",
|
|
"#create a binary column - 0 = gain, 1 = loss to have something to predict\n",
|
|
"date['Up/Down'] = np.where(date['pos_neg']>0, '0', '1')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "uZLR2R9WQyzR"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#create data sets for different crypto currencies\n",
|
|
"etherdate = date[date['symbol']=='ETH']\n",
|
|
"ltcdate = date[date['symbol']=='LTC']\n",
|
|
"tetherdate = date[date['symbol']=='USDT']\n",
|
|
"cardanodate = date[date['symbol']=='ADA']\n",
|
|
"rippledate = date[date['symbol']=='XRP']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "TQKkNj3wUM6B"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#concat the different frames into one\n",
|
|
"frames=[etherdate,ltcdate,tetherdate,cardanodate,rippledate]\n",
|
|
"five_crypto = pd.concat(frames)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "Zjb4kqCzVXwY",
|
|
"outputId": "66568d7f-6656-4101-913c-11e21f22e7e6"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"five_crypto.info()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 296
|
|
},
|
|
"id": "TXNuyzFoVevf",
|
|
"outputId": "3c7295fd-0ec6-4a3f-e09b-e81d5cc7adf8"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#this chart shows each of the six and the number of times they closed up for the day. \n",
|
|
"sns.set_style('whitegrid')\n",
|
|
"sns.countplot(x='symbol',hue='Up/Down',data=five_crypto,palette='rainbow')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 454
|
|
},
|
|
"id": "b-KaRiCFVlF3",
|
|
"outputId": "426f31e8-b3b3-44e6-ea05-641d58318b45"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#a comparison the the up/down ratio at the end of the day for the six.\n",
|
|
"sns.set(style=\"whitegrid\")\n",
|
|
"\n",
|
|
"g = sns.factorplot(\"symbol\", \"close_ratio\", \"Up/Down\",\n",
|
|
" data=five_crypto, kind=\"bar\",\n",
|
|
" size=6, palette=\"muted\",\n",
|
|
" legend_out=True)\n",
|
|
"g.despine(left=True)\n",
|
|
"g.set_ylabels(\"Ratio Count\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "S3ZOHw0xW5RK"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data['market_billion'] = data['market'] / 1000000000\n",
|
|
"data['volume_million'] = data['volume'] / 1000000000\n",
|
|
"data['volume_billion'] = data['volume']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 338
|
|
},
|
|
"id": "s86ZmNTCWvdf",
|
|
"outputId": "e1b87670-2d40-4b8e-d1c9-378782b9f330"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Let's prepare one dataframe where we will observe closing prices for each currency\n",
|
|
"wide_format = data.groupby(['date', 'name'])['close'].last().unstack()\n",
|
|
"wide_format.head(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 301
|
|
},
|
|
"id": "fKfl6z3bWxMP",
|
|
"outputId": "010fb6ee-6ac0-4fa5-a6b8-0ff102902d0a"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"ax = data.groupby(['name'])['market_billion'].last().sort_values(ascending=False).head(10).sort_values().plot(kind='barh');\n",
|
|
"ax.set_xlabel(\"Market cap (in billion USD)\");\n",
|
|
"plt.title(\"Top 10 Currencies by Market Cap\");"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 301
|
|
},
|
|
"id": "4MJQipOJXAlD",
|
|
"outputId": "0b8c53b6-4253-47e0-8c2f-d9f92a8ac7c1"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"ax = data.groupby(['name'])['volume_million'].last().sort_values(ascending=False).head(10).sort_values().plot(kind='barh');\n",
|
|
"ax.set_xlabel(\"Transaction Volume (in million)\");\n",
|
|
"plt.title(\"Top 10 Currencies by Transaction Volume\");"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "qfrzkuc2XbeX",
|
|
"outputId": "a182edbc-70a2-4d4d-9f72-3727d9c538fc"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# For sake of convenience, let's define the top 6 currencies\n",
|
|
"\n",
|
|
"top_6_currency_names = data.groupby(['name'])['market'].last().sort_values(ascending=False).head(6).index\n",
|
|
"data_top_6_currencies = data[data['name'].isin(top_6_currency_names)]\n",
|
|
"data_top_6_currencies.head(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 301
|
|
},
|
|
"id": "X2vurY0xXmff",
|
|
"outputId": "f08949e0-ec1b-4167-8875-99474ff74d57"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"ax = data_top_6_currencies.groupby(['date', 'name'])['close'].mean().unstack().plot();\n",
|
|
"ax.set_ylabel(\"Price per 1 unit (in USD)\");\n",
|
|
"plt.title(\"Price per unit currency\");"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 301
|
|
},
|
|
"id": "0XQYcAf8Xy2o",
|
|
"outputId": "5dc80b97-f28a-40b7-f929-13cb06120176"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"ax = data_top_6_currencies.groupby(['date', 'name'])['market_billion'].mean().unstack().plot();\n",
|
|
"ax.set_ylabel(\"Market Cap (in billion USD)\");\n",
|
|
"plt.title(\"Market cap per unit currency\");"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 506
|
|
},
|
|
"id": "_Ezp2S7NX9sI",
|
|
"outputId": "7da7fcc0-5908-4dd8-f980-bb7f8fce40da"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#co-relation between different cryptos\n",
|
|
"plt.figure(figsize=(14,8))\n",
|
|
"sns.heatmap(wide_format[top_6_currency_names].corr(),vmin=0, vmax=1, cmap='coolwarm', annot=True);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "NbAmRhK-CrQV",
|
|
"outputId": "e8338b77-ce5e-47ba-f17c-1b971f2d4b2b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_top_6_currencies.head(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 661
|
|
},
|
|
"id": "yy3-xD2qJ8lI",
|
|
"outputId": "cf7f6044-f55c-4ee1-c38b-47e99bd705bd"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_top_6_currencies.drop(['ranknow'],axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "O1V5XiRaF1DU",
|
|
"outputId": "d551e810-f579-4252-8c89-99a5b97df7ff"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_top_6_currencies.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "TNJ2Lwm3KDAX"
|
|
},
|
|
"source": [
|
|
"#Bitcoin"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "49_WACQuDBll",
|
|
"outputId": "5f524485-7bfd-4400-ac65-08819492e014"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#bitcoin\n",
|
|
"df1 = data_top_6_currencies[data_top_6_currencies['name']=='Bitcoin']\n",
|
|
"\n",
|
|
"df1.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "JjcbrGKDQTQC",
|
|
"outputId": "045951bb-0e3a-499b-ed16-8fcba13e5896"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(df1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "l6R9yP7G_7C9",
|
|
"outputId": "7ec05f9a-69a0-4b30-9b00-8062d5ab045a"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df1.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "X4QbNB76AUtO",
|
|
"outputId": "3853da87-8db7-483b-bd83-0d69a36e18bc"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"d = df1.iloc[:, 5:10]\n",
|
|
"print(d.head())\n",
|
|
"print(d.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "zDuTpvheHFQ-",
|
|
"outputId": "9c6fdee9-de24-4f66-d1c6-00c12a69dad4"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"sns.lineplot(x=d.index, y=\"open\", data=d).set_title(\"Price of Bitcoin\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "35cpNfDfHFUN",
|
|
"outputId": "23b39114-4fc4-42a3-d7a3-aeab60030a3b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = d.iloc[:, 0]\n",
|
|
"print(len(data))\n",
|
|
"print(data)\n",
|
|
"hist = []\n",
|
|
"target = []\n",
|
|
"length = 90\n",
|
|
"for i in range(len(data)-length):\n",
|
|
" x = data[i:i+length]\n",
|
|
" y = data[i+length]\n",
|
|
" hist.append(x)\n",
|
|
" target.append(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "ybiiTeB7HFad",
|
|
"outputId": "97d5b512-2429-44d3-bc73-5a94b9325916"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(hist[1][89])\n",
|
|
"print(data[90])\n",
|
|
"print(target[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "e5cpw-VsHTGw"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = np.array(hist)\n",
|
|
"target = np.array(target)\n",
|
|
"target = target.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "Ook8PYf9HTLD"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.preprocessing import MinMaxScaler\n",
|
|
"sc = MinMaxScaler()\n",
|
|
"hist_scaled = sc.fit_transform(hist)\n",
|
|
"target_scaled = sc.fit_transform(target)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "T_-csG_1HTON",
|
|
"outputId": "5fbb5f68-96e1-4f98-fae9-1dbb1fbf7e47"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
|
|
"print(hist_scaled.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "Ui0ppeSGHTRf"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train = hist_scaled[:1900,:,:]\n",
|
|
"X_test = hist_scaled[1900:,:,:]\n",
|
|
"y_train = target_scaled[:1900,:]\n",
|
|
"y_test = target_scaled[1900:,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "T5o66_7XHTUm"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "9C8DS-c7Hgp1",
|
|
"outputId": "e29c0ce0-4ca5-495a-eb3d-3a93be61950b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = tf.keras.Sequential()\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" input_shape=(90,1), dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, dropout=0.2))\n",
|
|
"model.add(layers.Dense(units=1))\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "P91hyF2GHgzR"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "I5uoPY81Hmy4",
|
|
"outputId": "08ad7eed-2807-415b-cca6-b7f1db79ebf9"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 502
|
|
},
|
|
"id": "_2skqc4uHoo4",
|
|
"outputId": "9334689b-73a1-48c3-9411-c5eff80540f8"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"loss = history.history['loss']\n",
|
|
"epoch_count = range(1, len(loss) + 1)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(epoch_count, loss, 'r--')\n",
|
|
"plt.legend(['Training Loss'])\n",
|
|
"plt.xlabel('Epoch')\n",
|
|
"plt.ylabel('Loss')\n",
|
|
"plt.show();"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 501
|
|
},
|
|
"id": "XeNxAYOlHg8b",
|
|
"outputId": "474939e5-3aea-401b-b312-ab3c434ca9b5"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = model.predict(X_test)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(y_test, color='blue', label='Real')\n",
|
|
"plt.plot(pred, color='red', label='Prediction')\n",
|
|
"plt.title('Bitcoin Price Prediction')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "I7VuAnbfJByj"
|
|
},
|
|
"source": [
|
|
"#XRP (Ripple)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "ZK38bhh6JByj",
|
|
"outputId": "022a7b36-5a24-43b5-bfb1-e14b29dc6af1"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#bitcoin\n",
|
|
"df2 = data_top_6_currencies[data_top_6_currencies['name']=='XRP']\n",
|
|
"\n",
|
|
"df2.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "jV8de94uJByj",
|
|
"outputId": "16f68219-e348-47fe-ec42-156427813871"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(df2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "vT1hHSzbJByk",
|
|
"outputId": "0c15cfeb-ac61-446b-97ae-91a31b813235"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df2.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "bOfzu0IhJByk",
|
|
"outputId": "99041c2b-6683-45d9-fee7-3d9e2ba89101"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"d2 = df2.iloc[:, 5:10]\n",
|
|
"print(d2.head())\n",
|
|
"print(d2.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "hSNYPEm7JByk",
|
|
"outputId": "b1bb1d23-7a54-4439-d8fa-f204886cab79"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"sns.lineplot(x=d2.index, y=\"open\", data=d2).set_title(\"Price of Ripple\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "pcllm_j2JByk",
|
|
"outputId": "effc2f69-4149-4390-be0e-f260f5ae6ec2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = d2.iloc[:, 0]\n",
|
|
"\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "JoAgH76ZLPjI"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = []\n",
|
|
"target = []\n",
|
|
"length = 90\n",
|
|
"for i in range(2042,len(data)-length):\n",
|
|
" x = data[2042:i+length]\n",
|
|
" y = data[i+length]\n",
|
|
" hist.append(x)\n",
|
|
" target.append(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "jGSDstNAJByl"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = np.array(hist)\n",
|
|
"target = np.array(target)\n",
|
|
"target = target.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "FpugPkG9NAtl"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = hist.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 381
|
|
},
|
|
"id": "icGR5bLNJByl",
|
|
"outputId": "2e8b762b-8ce2-4d91-bd44-a006a6fe559e"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.preprocessing import MinMaxScaler\n",
|
|
"sc = MinMaxScaler()\n",
|
|
"hist_scaled = sc.fit_transform(hist)\n",
|
|
"target_scaled = sc.fit_transform(target)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "AXyWLswgJByl",
|
|
"outputId": "27002554-6aff-4173-bfcc-dff86d04a1d2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
|
|
"print(hist_scaled.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "j9I5SQkPJByl"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train = hist_scaled[:1900,:,:]\n",
|
|
"X_test = hist_scaled[1900:,:,:]\n",
|
|
"y_train = target_scaled[:1900,:]\n",
|
|
"y_test = target_scaled[1900:,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "wMmrZ7hFJByl"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "Tfb5_8l6JByl",
|
|
"outputId": "c59503b3-e2cf-4ccc-ab5a-3d487da4d767"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = tf.keras.Sequential()\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" input_shape=(90,1), dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, dropout=0.2))\n",
|
|
"model.add(layers.Dense(units=1))\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "XblbdTVWJByl"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "wCu3FeoiJBym",
|
|
"outputId": "9e95db9e-0329-42e8-a740-f56c4c8f1ce1"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 501
|
|
},
|
|
"id": "jqnFts9HJBym",
|
|
"outputId": "4d9124d3-4f27-4678-d728-7037c6b723fa"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = model.predict(X_test)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(y_test, color='blue', label='Real')\n",
|
|
"plt.plot(pred, color='red', label='Prediction')\n",
|
|
"plt.title('Ripple Price Prediction')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "HVw69BHsP-SH"
|
|
},
|
|
"source": [
|
|
"#Ethereum"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "6Gf_B5zeP-SI",
|
|
"outputId": "f8b449f2-2861-485b-a856-d34e57c17867"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#bitcoin\n",
|
|
"df3 = data_top_6_currencies[data_top_6_currencies['slug']=='ethereum']\n",
|
|
"\n",
|
|
"df3.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "wTrWsHkZP-SI",
|
|
"outputId": "5790481a-51d7-4fcf-c069-acb926892bcb"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(df3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "zsvIQYO3P-SI",
|
|
"outputId": "57dc9c1a-5cb2-47d2-9a82-b99842248a94"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df3.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "TnheAbnAP-SI",
|
|
"outputId": "f3948958-bb36-4936-8eac-86179eaf36f2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"d3 = df3.iloc[:, 5:10]\n",
|
|
"print(d3.head())\n",
|
|
"print(d3.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "T3xP1Eo8P-SJ",
|
|
"outputId": "e6c83d5e-0291-41a2-94c7-91cc5afb6104"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"sns.lineplot(x=d3.index, y=\"open\", data=d3).set_title(\"Price of Ethereum\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "kbwR0m7RP-SJ",
|
|
"outputId": "4880a7a3-544e-4a9c-c33d-ca7f1e7d0221"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = d3.iloc[:, 0]\n",
|
|
"\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "KPzSexH2P-SJ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = []\n",
|
|
"target = []\n",
|
|
"length = 90\n",
|
|
"for i in range(3986,len(data)-length):\n",
|
|
" x = data[3986:i+length]\n",
|
|
" y = data[i+length]\n",
|
|
" hist.append(x)\n",
|
|
" target.append(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "I5QVQECtP-SJ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = np.array(hist)\n",
|
|
"target = np.array(target)\n",
|
|
"target = target.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "rhe9sKH3P-SJ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = hist.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "FNSDMq0mP-SK",
|
|
"outputId": "e90ec60d-7d53-468e-cc15-43f4f25b1dba"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
|
|
"print(hist_scaled.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "bBrwRQLsP-SK"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train = hist_scaled[:1900,:,:]\n",
|
|
"X_test = hist_scaled[1900:,:,:]\n",
|
|
"y_train = target_scaled[:1900,:]\n",
|
|
"y_test = target_scaled[1900:,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "zvGPHlb2P-SK"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "aWFjURq-P-SK",
|
|
"outputId": "f941254d-4964-4573-85c7-1ff0fe062ca8"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = tf.keras.Sequential()\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" input_shape=(90,1), dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, dropout=0.2))\n",
|
|
"model.add(layers.Dense(units=1))\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "gcUc1ZBHP-SK"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "ZfAB0JWTP-SK",
|
|
"outputId": "45985017-af61-43c6-cc29-d85d10c38a68"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 504
|
|
},
|
|
"id": "DT2SuvDdP-SK",
|
|
"outputId": "1aa682fa-9709-457f-845d-a6c5b4b8d126"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"loss = history.history['loss']\n",
|
|
"epoch_count = range(1, len(loss) + 1)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(epoch_count, loss, 'r--')\n",
|
|
"plt.legend(['Training Loss'])\n",
|
|
"plt.xlabel('Epoch')\n",
|
|
"plt.ylabel('Loss')\n",
|
|
"plt.show();"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 501
|
|
},
|
|
"id": "eArbMF4TP-SL",
|
|
"outputId": "306dcd95-93db-47f1-8c32-c97757c9b584"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = model.predict(X_test)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(y_test, color='blue', label='Real')\n",
|
|
"plt.plot(pred, color='red', label='Prediction')\n",
|
|
"plt.title('Ethereum Price Prediction')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "k4_p4ZjbUJWo"
|
|
},
|
|
"source": [
|
|
"#Bitcoin Cash (BCH)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "O4H46vFAUJWo",
|
|
"outputId": "515cdde8-18ad-4f16-effa-dd66d0ac6380"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"df4 = data_top_6_currencies[data_top_6_currencies['symbol']=='BCH']\n",
|
|
"\n",
|
|
"df4.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "5-4itcj_UJWo",
|
|
"outputId": "35e6401a-92fb-4f3d-d845-4246d7e5c05f"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(df4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "mn_n52xrUJWo",
|
|
"outputId": "b2088956-9bf1-4b69-ade6-ef1049fd8f15"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df4.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "lNOi437hUJWp",
|
|
"outputId": "4acc7d16-f0b8-4458-86b6-8b895f60776b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"d4 = df4.iloc[:, 5:10]\n",
|
|
"print(d4.head())\n",
|
|
"print(d4.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "WHrBesaXUJWp",
|
|
"outputId": "5a43e17c-b851-4460-a112-9e7f950c115f"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"sns.lineplot(x=d4.index, y=\"open\", data=d4).set_title(\"Price of Bitcoin Cash\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "YIlXzSaUUJWp",
|
|
"outputId": "e4214da2-9147-4e4f-ddbf-60224a7ab83e"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = d4.iloc[:, 0]\n",
|
|
"\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "sZeAotUnUJWp"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = []\n",
|
|
"target = []\n",
|
|
"length = 90\n",
|
|
"for i in range(6775,len(data)-length):\n",
|
|
" x = data[6775:i+length]\n",
|
|
" y = data[i+length]\n",
|
|
" hist.append(x)\n",
|
|
" target.append(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "SBJAZqjGUJWp"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = np.array(hist)\n",
|
|
"target = np.array(target)\n",
|
|
"target = target.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "jLGddq22UJWp"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = hist.reshape(1,-1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "hC4XeVpMUJWq",
|
|
"outputId": "d822ff1e-cb56-4e83-cb99-c7792309c96c"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
|
|
"print(hist_scaled.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "xctoaT_KUJWq"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train = hist_scaled[:1900,:,:]\n",
|
|
"X_test = hist_scaled[1900:,:,:]\n",
|
|
"y_train = target_scaled[:1900,:]\n",
|
|
"y_test = target_scaled[1900:,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "kTzfwpunUJWq"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "RoZrj801UJWq",
|
|
"outputId": "5bcea4f2-4c03-4c59-d37f-1330e4e8795b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = tf.keras.Sequential()\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" input_shape=(90,1), dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, dropout=0.2))\n",
|
|
"model.add(layers.Dense(units=1))\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "bb-ArUEIUJWq"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "bQebGMcsUJWq",
|
|
"outputId": "aa752aaa-dbd6-4aac-cae6-42f18e6f7b1d"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 501
|
|
},
|
|
"id": "UH77ijSZUJWq",
|
|
"outputId": "9009ae84-499d-4c8d-d18e-dc8fc479ecb7"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = model.predict(X_test)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(y_test, color='blue', label='Real')\n",
|
|
"plt.plot(pred, color='red', label='Prediction')\n",
|
|
"plt.title('Bitcoin Cash Prediction')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "zIyf6qzoX2oL"
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "pBEwefqtX3kW"
|
|
},
|
|
"source": [
|
|
"#EOS"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "TjCsw9DNX3kW",
|
|
"outputId": "c44e67f8-b66c-4c41-c898-73f7821629aa"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df5 = data_top_6_currencies[data_top_6_currencies['symbol']=='EOS']\n",
|
|
"\n",
|
|
"df5.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "7VzpsN7TX3kW",
|
|
"outputId": "81f3990c-1939-4fe5-818f-8e597166f8f6"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(df5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "e-d8UPiHX3kW",
|
|
"outputId": "253e678c-66a2-4f72-d8b9-5422a9fee9e6"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df5.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "stGfWD9YX3kX",
|
|
"outputId": "cbaee387-a35d-483d-d0b4-3c4c6d1293e8"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"d5 = df5.iloc[:, 5:10]\n",
|
|
"print(d5.head())\n",
|
|
"print(d5.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "Y8SdLCNNX3kX",
|
|
"outputId": "2e8edc3b-5e7a-4684-8318-4e6229b17166"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"sns.lineplot(x=d5.index, y=\"open\", data=d5).set_title(\"Price of EOS\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "wvdmLlFsX3kX",
|
|
"outputId": "6a0d3dad-bb9c-4325-cae5-959f222c9ac3"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = d5.iloc[:, 0]\n",
|
|
"\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "yawhGJ_rX3kX"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = []\n",
|
|
"target = []\n",
|
|
"length = 90\n",
|
|
"for i in range(7270,len(data)-length):\n",
|
|
" x = data[7270:i+length]\n",
|
|
" y = data[i+length]\n",
|
|
" hist.append(x)\n",
|
|
" target.append(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "ldt2BGLhX3kX"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = np.array(hist)\n",
|
|
"target = np.array(target)\n",
|
|
"target = target.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "ylr7fwbAX3kX"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = hist.reshape(1,-1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "ejlHo8paX3kX",
|
|
"outputId": "49925969-2cfb-42de-9d6e-c1e695866eec"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
|
|
"print(hist_scaled.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "TOFbdITuX3kX"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train = hist_scaled[:1900,:,:]\n",
|
|
"X_test = hist_scaled[1900:,:,:]\n",
|
|
"y_train = target_scaled[:1900,:]\n",
|
|
"y_test = target_scaled[1900:,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "2q1aJEr5X3kY"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "zAml_KZ4X3kY",
|
|
"outputId": "0c411e7b-36d4-4ac5-9f14-b2bdac508d0b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = tf.keras.Sequential()\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" input_shape=(90,1), dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, dropout=0.2))\n",
|
|
"model.add(layers.Dense(units=1))\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "blIv5kwJX3kY"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "PYbQNxSRX3kY",
|
|
"outputId": "9a4cbc6e-4075-47e9-820f-083cdc6c59f5"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 501
|
|
},
|
|
"id": "TT6qgo_iX3kY",
|
|
"outputId": "cb71873e-b886-4e41-9947-f2b3765d2f6e"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = model.predict(X_test)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(y_test, color='blue', label='Real')\n",
|
|
"plt.plot(pred, color='red', label='Prediction')\n",
|
|
"plt.title('EOS Prediction')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "F087Ykqwazgx"
|
|
},
|
|
"source": [
|
|
"#Stellar"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "gSZO1_fBa5Oe",
|
|
"outputId": "e7db4e69-1838-409d-e2fe-1c8c92614a2c"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df6 = data_top_6_currencies[data_top_6_currencies['name']=='Stellar']\n",
|
|
"\n",
|
|
"df6.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "mejmybDCa5Of",
|
|
"outputId": "573b4195-a4fd-47c3-a924-2579a992512e"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(df6)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 357
|
|
},
|
|
"id": "6jooY2Gza5Of",
|
|
"outputId": "cb00a2dd-c0ba-4ade-9a3f-5f769a78a2bb"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df6.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "u4YVMNKha5Of",
|
|
"outputId": "33a752e7-ee5a-4f3b-be07-032b4400973e"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"d = df6.iloc[:, 5:10]\n",
|
|
"print(d.head())\n",
|
|
"print(d.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "Pib0FOL3a5Of",
|
|
"outputId": "64bbcef9-615a-45c8-817c-d607016ab130"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"sns.lineplot(x=d.index, y=\"open\", data=d).set_title(\"Price of Stellar\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "1FPPamzibSiL",
|
|
"outputId": "05da7d73-8e57-4f94-b52d-da5d4cf97102"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(d)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "O5snSz7Aa5Of",
|
|
"outputId": "83f62e46-1b7f-4825-cc84-2128c9d2c5be"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = d.iloc[:, 0]\n",
|
|
"print(len(data))\n",
|
|
"print(data)\n",
|
|
"hist = []\n",
|
|
"target = []\n",
|
|
"length = 90\n",
|
|
"for i in range(5197, len(data)-length):\n",
|
|
" x = data[5197,:i+length]\n",
|
|
" y = data[i+length]\n",
|
|
" hist.append(x)\n",
|
|
" target.append(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "5wCLydLha5Og"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist = np.array(hist)\n",
|
|
"target = np.array(target)\n",
|
|
"target = target.reshape(-1,1)\n",
|
|
"hist=hist.reshape(-1,1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "PxEu7xN0a5Og"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.preprocessing import MinMaxScaler\n",
|
|
"sc = MinMaxScaler()\n",
|
|
"hist_scaled = sc.fit_transform(hist)\n",
|
|
"target_scaled = sc.fit_transform(target)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "JBgXLlZAa5Og",
|
|
"outputId": "371d59f2-bdfe-4e90-b328-3ba17be8f29f"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"hist_scaled = hist_scaled.reshape((len(hist_scaled), length, 1))\n",
|
|
"print(hist_scaled.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "wmqhRFVUa5Og"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train = hist_scaled[:1900,:,:]\n",
|
|
"X_test = hist_scaled[1900:,:,:]\n",
|
|
"y_train = target_scaled[:1900,:]\n",
|
|
"y_test = target_scaled[1900:,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "hKMNQyGna5Og"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "wIUvXFUCa5Og",
|
|
"outputId": "7a104795-05f8-411f-94f9-314eb6c46b84"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = tf.keras.Sequential()\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" input_shape=(90,1), dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, return_sequences=True,\n",
|
|
" dropout=0.2))\n",
|
|
"model.add(layers.LSTM(units=32, dropout=0.2))\n",
|
|
"model.add(layers.Dense(units=1))\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "2JVd7TxPa5Og"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer='adam', loss='mean_squared_error')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "m5lWh3Yba5Oh",
|
|
"outputId": "2846ed4f-6ace-4a2f-b80c-b580aba9a176"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(X_train, y_train, epochs=30, batch_size=32)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 501
|
|
},
|
|
"id": "bCmQ1HDRa5Oh",
|
|
"outputId": "ed8ad8b9-4bb2-4563-f756-26dde67ca6eb"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = model.predict(X_test)\n",
|
|
"plt.figure(figsize=(12,8))\n",
|
|
"plt.plot(y_test, color='blue', label='Real')\n",
|
|
"plt.plot(pred, color='red', label='Prediction')\n",
|
|
"plt.title('Stellar Price Prediction')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [
|
|
"W06FRpNCHKCo",
|
|
"sQ_p1oakHNPn",
|
|
"bGQUmu60NwRD",
|
|
"VJIl1DwwN43f"
|
|
],
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.10.7 64-bit (microsoft store)",
|
|
"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": "3.10.11"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "94348be3325f1ae160a67cb39ab80a1e458b4a3911d8ece2b1e6db7371f6660e"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|