{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"![image.png](attachment:2e89556b-2524-440f-9852-c296e99f6bef.png)","metadata":{},"attachments":{"2e89556b-2524-440f-9852-c296e99f6bef.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"[The below data description copied from](https://www.kaggle.com/c/g-research-crypto-forecasting/data)","metadata":{}},{"cell_type":"markdown","source":"#  DATA DESCRIPTION\n\n# Files\n\n### train.csv - The training set\n\n* timestamp - A timestamp for the minute covered by the row.\n* Asset_ID - An ID code for the cryptoasset.\n* Count - The number of trades that took place this minute.\n* Open - The USD price at the beginning of the minute.\n* High - The highest USD price during the minute.\n* Low - The lowest USD price during the minute.\n* Close - The USD price at the end of the minute.\n* Volume - The number of cryptoasset units traded during the minute.\n* VWAP - The volume weighted average price for the minute.\n* Target - 15 minute residualized returns. See the 'Prediction and Evaluation' section of this notebook for details of how the target is calculated.\n\n","metadata":{}},{"cell_type":"markdown","source":"##### example_test.csv - An example of the data that will be delivered by the time series API.\n##### example_sample_submission.csv - An example of the data that will be delivered by the time series API. The data is just copied from train.csv.\n##### asset_details.csv - Provides the real name and of the cryptoasset for each Asset_ID and the weight each cryptoasset receives in the metric.\n##### gresearch_crypto - An unoptimized version of the time series API files for offline work. You may need Python 3.7 and a Linux environment to run it without errors.\n##### supplemental_train.csv - After the submission period is over this file's data will be replaced with cryptoasset prices from the submission period. The current copy, which is just filled approximately the right amount of data from train.csv is provided as a placeholder.","metadata":{}},{"cell_type":"code","source":"!pip -q install mplfinance\nimport mplfinance as mpf","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:08:14.785957Z","iopub.execute_input":"2021-11-06T16:08:14.786552Z","iopub.status.idle":"2021-11-06T16:08:23.959939Z","shell.execute_reply.started":"2021-11-06T16:08:14.786451Z","shell.execute_reply":"2021-11-06T16:08:23.958815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd, numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom itertools import cycle\nimport datetime as dt\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Activation, Dense, Dropout, LSTM\npd.set_option('max_columns', 50)\n\nplt.style.use('dark_background')\n\ncolor_pal = plt.rcParams['axes.prop_cycle'].by_key()['color']\ncolor_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:08:23.961858Z","iopub.execute_input":"2021-11-06T16:08:23.962120Z","iopub.status.idle":"2021-11-06T16:08:30.664140Z","shell.execute_reply.started":"2021-11-06T16:08:23.962092Z","shell.execute_reply":"2021-11-06T16:08:30.663258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('../input/g-research-crypto-forecasting/train.csv')\nasset_df = pd.read_csv('../input/g-research-crypto-forecasting/asset_details.csv')\nsupplement_train_df = pd.read_csv('../input/g-research-crypto-forecasting/supplemental_train.csv')","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:08:30.665549Z","iopub.execute_input":"2021-11-06T16:08:30.665862Z","iopub.status.idle":"2021-11-06T16:09:28.523987Z","shell.execute_reply.started":"2021-11-06T16:08:30.665823Z","shell.execute_reply":"2021-11-06T16:09:28.522851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print (train_df.shape)\nprint (asset_df.shape)\nprint (supplement_train_df.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:28.525333Z","iopub.execute_input":"2021-11-06T16:09:28.525706Z","iopub.status.idle":"2021-11-06T16:09:28.532868Z","shell.execute_reply.started":"2021-11-06T16:09:28.525659Z","shell.execute_reply":"2021-11-06T16:09:28.531873Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"asset_df.head(15)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:09:28.536205Z","iopub.execute_input":"2021-11-06T16:09:28.536548Z","iopub.status.idle":"2021-11-06T16:09:28.562664Z","shell.execute_reply.started":"2021-11-06T16:09:28.536507Z","shell.execute_reply":"2021-11-06T16:09:28.561946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"asset_df['wgt_pct'] = asset_df['Weight'] /asset_df['Weight'].sum()*100","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:28.565803Z","iopub.execute_input":"2021-11-06T16:09:28.566043Z","iopub.status.idle":"2021-11-06T16:09:28.575269Z","shell.execute_reply.started":"2021-11-06T16:09:28.566015Z","shell.execute_reply":"2021-11-06T16:09:28.574174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"explode = [0, 0, 0, 0, 0,0,0,0, 0,0,0,0.08,0.15, 0.18]\n\ndef func(pct, allvals):\n    absolute = int(round(pct/100.*np.sum(allvals)))\n    return \"{:.1f}%\\n({:d} g)\".format(pct, absolute)\n\n\nax = asset_df.set_index('Asset_Name')['wgt_pct'] \\\n    .sort_values(ascending=False) \\\n    .plot(kind='pie',\n          figsize=(22, 12),\n          fontsize = 14,\n          textprops={'color':\"magenta\"}, \n           autopct='%0.2f%%',\n          explode = explode,\n          shadow = True,\n          startangle=90, \n         )\nax.legend(asset_df['Asset_Name'],\n          loc =\"upper right\",\n          fontsize = 14,\n          bbox_to_anchor =(1, 0, 0.5, 1))\n\n\nax.set_title('Assets by Weight Percentage\\n\\n ', fontsize=20, color = 'cyan')\nax.set_ylabel('')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:28.576637Z","iopub.execute_input":"2021-11-06T16:09:28.576932Z","iopub.status.idle":"2021-11-06T16:09:29.042501Z","shell.execute_reply.started":"2021-11-06T16:09:28.576894Z","shell.execute_reply":"2021-11-06T16:09:29.041456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"id_mapping = dict(asset_df[['Asset_ID', 'Asset_Name']].values)\nwgt_mapping = dict(asset_df[['Asset_ID', 'Weight']].values)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:29.043843Z","iopub.execute_input":"2021-11-06T16:09:29.044083Z","iopub.status.idle":"2021-11-06T16:09:29.051394Z","shell.execute_reply.started":"2021-11-06T16:09:29.044055Z","shell.execute_reply":"2021-11-06T16:09:29.050516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df[\"Asset name\"] = train_df[\"Asset_ID\"].map(id_mapping)\ntrain_df[\"Asset weight\"] = train_df[\"Asset_ID\"].map(wgt_mapping)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:29.054307Z","iopub.execute_input":"2021-11-06T16:09:29.054889Z","iopub.status.idle":"2021-11-06T16:09:30.534326Z","shell.execute_reply.started":"2021-11-06T16:09:29.054833Z","shell.execute_reply":"2021-11-06T16:09:30.533501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:30.535323Z","iopub.execute_input":"2021-11-06T16:09:30.535553Z","iopub.status.idle":"2021-11-06T16:09:30.553203Z","shell.execute_reply.started":"2021-11-06T16:09:30.535528Z","shell.execute_reply":"2021-11-06T16:09:30.552256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlation**","metadata":{}},{"cell_type":"code","source":"corr = train_df.corr()\nmask = np.zeros_like(corr)\nmask[np.triu_indices_from(mask)] = True\n#with sns.axes_style(\"darkgrid\"):\nf, ax = plt.subplots(figsize=(15, 8))\nax = sns.heatmap(corr,mask=mask, annot = True, cmap = 'PiYG')\n","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:30.554362Z","iopub.execute_input":"2021-11-06T16:09:30.554899Z","iopub.status.idle":"2021-11-06T16:09:44.843871Z","shell.execute_reply.started":"2021-11-06T16:09:30.554864Z","shell.execute_reply":"2021-11-06T16:09:44.842898Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Query only ethereum for the analysis","metadata":{}},{"cell_type":"code","source":"ethereum = train_df.query(\"Asset_ID == 6\").reset_index(drop = True)\nethereum['date'] = pd.to_datetime(ethereum['timestamp'], unit='s')\nethereum = ethereum.set_index('date')","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:44.845546Z","iopub.execute_input":"2021-11-06T16:09:44.846178Z","iopub.status.idle":"2021-11-06T16:09:48.230186Z","shell.execute_reply.started":"2021-11-06T16:09:44.846130Z","shell.execute_reply":"2021-11-06T16:09:48.229220Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ethereum.shape[0]","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:48.232512Z","iopub.execute_input":"2021-11-06T16:09:48.232858Z","iopub.status.idle":"2021-11-06T16:09:48.239598Z","shell.execute_reply.started":"2021-11-06T16:09:48.232814Z","shell.execute_reply":"2021-11-06T16:09:48.238317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ethereum[ethereum['Target'].isnull()]\n","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:48.243927Z","iopub.execute_input":"2021-11-06T16:09:48.244170Z","iopub.status.idle":"2021-11-06T16:09:48.433491Z","shell.execute_reply.started":"2021-11-06T16:09:48.244143Z","shell.execute_reply":"2021-11-06T16:09:48.432510Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ethereum.index.min(), ethereum.index.max()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:48.435027Z","iopub.execute_input":"2021-11-06T16:09:48.435359Z","iopub.status.idle":"2021-11-06T16:09:48.448238Z","shell.execute_reply.started":"2021-11-06T16:09:48.435316Z","shell.execute_reply":"2021-11-06T16:09:48.447451Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpf.plot(ethereum, type = 'renko')","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:48.450336Z","iopub.execute_input":"2021-11-06T16:09:48.450695Z","iopub.status.idle":"2021-11-06T16:09:58.724267Z","shell.execute_reply.started":"2021-11-06T16:09:48.450650Z","shell.execute_reply":"2021-11-06T16:09:58.723535Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_ = ethereum.loc['2021-04-01 05:45:00':'2021-04-01 8:00:00']","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:58.725452Z","iopub.execute_input":"2021-11-06T16:09:58.725677Z","iopub.status.idle":"2021-11-06T16:09:58.765377Z","shell.execute_reply.started":"2021-11-06T16:09:58.725651Z","shell.execute_reply":"2021-11-06T16:09:58.764460Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpf.plot(sample_, # the dataframe containing the OHLC (Open, High, Low and Close) data\n         type='candle', # use candlesticks \n         volume=True, # also show the volume\n         mav=(10, 20), # use three different moving averages\n         figratio=(3,1), # set the ratio of the figure\n         style='yahoo',  # choose the yahoo style\n         title='Single day - short time frame transactions ');","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:58.766575Z","iopub.execute_input":"2021-11-06T16:09:58.766899Z","iopub.status.idle":"2021-11-06T16:09:59.446661Z","shell.execute_reply.started":"2021-11-06T16:09:58.766865Z","shell.execute_reply":"2021-11-06T16:09:59.445776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Show Intra-day","metadata":{}},{"cell_type":"code","source":"sample_1 = ethereum.loc['2021-09-02 05:45:00':'2021-09-02 07:00:00']","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:59.447828Z","iopub.execute_input":"2021-11-06T16:09:59.448062Z","iopub.status.idle":"2021-11-06T16:09:59.455656Z","shell.execute_reply.started":"2021-11-06T16:09:59.448035Z","shell.execute_reply":"2021-11-06T16:09:59.454191Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpf.plot(sample_1,type='line',mav=(5,15),volume=True)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:59.457009Z","iopub.execute_input":"2021-11-06T16:09:59.457283Z","iopub.status.idle":"2021-11-06T16:09:59.994203Z","shell.execute_reply.started":"2021-11-06T16:09:59.457242Z","shell.execute_reply":"2021-11-06T16:09:59.993160Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Two Consequetive Day Transactions","metadata":{}},{"cell_type":"code","source":"iday = ethereum.loc['2020-11-05':'2020-11-06',:]","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:09:59.995400Z","iopub.execute_input":"2021-11-06T16:09:59.995659Z","iopub.status.idle":"2021-11-06T16:10:00.002158Z","shell.execute_reply.started":"2021-11-06T16:09:59.995629Z","shell.execute_reply":"2021-11-06T16:10:00.001308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mpf.plot(iday,type='line',volume=True, warn_too_much_data=50, mav=(25,50))","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:00.003648Z","iopub.execute_input":"2021-11-06T16:10:00.003886Z","iopub.status.idle":"2021-11-06T16:10:05.522380Z","shell.execute_reply.started":"2021-11-06T16:10:00.003857Z","shell.execute_reply":"2021-11-06T16:10:05.521509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Simple LSTM ","metadata":{}},{"cell_type":"code","source":"#Drop null values\n\"\"\"\nanalyse single asset \n\"\"\"\nethereum.dropna(axis = 0, inplace = True)\nethereum.shape","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:05.523685Z","iopub.execute_input":"2021-11-06T16:10:05.524553Z","iopub.status.idle":"2021-11-06T16:10:05.892660Z","shell.execute_reply.started":"2021-11-06T16:10:05.524510Z","shell.execute_reply":"2021-11-06T16:10:05.891753Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ethereum.head(5)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:10:05.893903Z","iopub.execute_input":"2021-11-06T16:10:05.894129Z","iopub.status.idle":"2021-11-06T16:10:05.912132Z","shell.execute_reply.started":"2021-11-06T16:10:05.894104Z","shell.execute_reply":"2021-11-06T16:10:05.911487Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ethereum[ethereum.index == '2021-01-01 00:01:00']","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:10:05.913459Z","iopub.execute_input":"2021-11-06T16:10:05.913805Z","iopub.status.idle":"2021-11-06T16:10:05.916829Z","shell.execute_reply.started":"2021-11-06T16:10:05.913777Z","shell.execute_reply":"2021-11-06T16:10:05.916209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Lets find the yearwise transaction pattern for ethereum","metadata":{}},{"cell_type":"code","source":"\nethereum.reset_index()\nethereum = ethereum.set_index('timestamp')","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:05.917785Z","iopub.execute_input":"2021-11-06T16:10:05.918468Z","iopub.status.idle":"2021-11-06T16:10:06.173554Z","shell.execute_reply.started":"2021-11-06T16:10:05.918401Z","shell.execute_reply":"2021-11-06T16:10:06.172679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"####https://www.kaggle.com/sami116/first-steps-crypto-forecasting\nf = plt.figure(figsize = (10,6))\n\nax = f.add_subplot(111)\nax.set_facecolor('azure')\nplt.plot(ethereum['Close'], c = 'darkviolet')\nplt.axvline(x = 1514764860, label = 'Start of 2018', c = 'gray')\nplt.axvline(x = 1546300860, label = 'Start of 2019', c = 'orange')\nplt.axvline(x = 1577836860, label = 'Start of 2020', c = 'forestgreen')\nplt.axvline(x = 1609459260, label = 'Start of 2021', c = 'crimson')\nplt.legend()\nplt.xlabel('Time')\nplt.ylabel('Ethereum Close')\nplt.grid()\nplt.title('Close price of ethereum')\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:06.174610Z","iopub.execute_input":"2021-11-06T16:10:06.174842Z","iopub.status.idle":"2021-11-06T16:10:13.163730Z","shell.execute_reply.started":"2021-11-06T16:10:06.174813Z","shell.execute_reply":"2021-11-06T16:10:13.162867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Its good to take 2021 data for prediction as compared to previous years ","metadata":{}},{"cell_type":"markdown","source":"# **[The below model is taken from the case study , ](https://thinkingneuron.com/predicting-stock-prices-using-deep-learning-lstm-model-in-python/)**","metadata":{}},{"cell_type":"markdown","source":"# Preparing the data","metadata":{}},{"cell_type":"code","source":"ethereum.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.165068Z","iopub.execute_input":"2021-11-06T16:10:13.165292Z","iopub.status.idle":"2021-11-06T16:10:13.181862Z","shell.execute_reply.started":"2021-11-06T16:10:13.165267Z","shell.execute_reply":"2021-11-06T16:10:13.180867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ethereum.reset_index(inplace = True)\nethereum['date'] = pd.to_datetime(ethereum['timestamp'], unit='s')\nethereum = ethereum.set_index('date')\nethereum.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.183238Z","iopub.execute_input":"2021-11-06T16:10:13.183531Z","iopub.status.idle":"2021-11-06T16:10:13.568255Z","shell.execute_reply.started":"2021-11-06T16:10:13.183488Z","shell.execute_reply":"2021-11-06T16:10:13.567496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"FullData = ethereum.loc['2021-05-01 00:01:00':'2021-06-30 23:59:59']\nFullData.head()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.569518Z","iopub.execute_input":"2021-11-06T16:10:13.569733Z","iopub.status.idle":"2021-11-06T16:10:13.622604Z","shell.execute_reply.started":"2021-11-06T16:10:13.569709Z","shell.execute_reply":"2021-11-06T16:10:13.621818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Extracting the closing prices of each day\nFullData=FullData[['Target']].values \n                  #ethereum[:25000][['Target']].values\nprint(FullData[0:5])\n \n# Feature Scaling for fast training of neural networks\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\n \n# Choosing between Standardization or normalization\n#sc = StandardScaler()\nsc=MinMaxScaler()\n \nDataScaler = sc.fit(FullData)\nX=DataScaler.transform(FullData)\n#X=FullData\n \nprint('### After Normalization ###')\nX[0:5]","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.623910Z","iopub.execute_input":"2021-11-06T16:10:13.624112Z","iopub.status.idle":"2021-11-06T16:10:13.640592Z","shell.execute_reply.started":"2021-11-06T16:10:13.624088Z","shell.execute_reply":"2021-11-06T16:10:13.639273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Prepare LSTM inputs","metadata":{}},{"cell_type":"code","source":"# split into samples\nX_samples = list()\ny_samples = list()\n\nNumerOfRows = len(X)\nTimeSteps=15  # next day's Price Prediction is based on last how many past day's prices\n\n# Iterate thru the values to create combinations\nfor i in range(TimeSteps , NumerOfRows , 1):\n    x_sample = X[i-TimeSteps:i]\n    y_sample = X[i]\n    X_samples.append(x_sample)\n    y_samples.append(y_sample)\n\n################################################\n# Reshape the Input as a 3D (number of samples, Time Steps, Features)\nX_data=np.array(X_samples)\nX_data=X_data.reshape(X_data.shape[0],X_data.shape[1], 1)\nprint('\\n#### Input Data shape ####')\nprint(X_data.shape)\n\n# We do not reshape y as a 3D data  as it is supposed to be a single column only\ny_data=np.array(y_samples)\ny_data=y_data.reshape(y_data.shape[0], 1)\nprint('\\n#### Output Data shape ####')\nprint(y_data.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.641846Z","iopub.execute_input":"2021-11-06T16:10:13.642187Z","iopub.status.idle":"2021-11-06T16:10:13.808563Z","shell.execute_reply.started":"2021-11-06T16:10:13.642151Z","shell.execute_reply":"2021-11-06T16:10:13.807859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train Test Split","metadata":{}},{"cell_type":"code","source":"# Choosing the number of validation data records\nTestingRecords=500\n \n# Splitting the data into train and test\nX_train=X_data[:-TestingRecords]\nX_test=X_data[-TestingRecords:]\ny_train=y_data[:-TestingRecords]\ny_test=y_data[-TestingRecords:]\n \n############################################\n \n# Printing the shape of training and testing\nprint('\\n#### Training Data shape ####')\nprint(X_train.shape)\nprint(y_train.shape)\nprint('\\n#### Validation Data shape ####')\nprint(X_test.shape)\nprint(y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.809630Z","iopub.execute_input":"2021-11-06T16:10:13.810040Z","iopub.status.idle":"2021-11-06T16:10:13.816377Z","shell.execute_reply.started":"2021-11-06T16:10:13.810010Z","shell.execute_reply":"2021-11-06T16:10:13.815505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Visualize the input output data for LSTM","metadata":{}},{"cell_type":"code","source":"\n# Visualizing the input and output being sent to the LSTM model\nfor inp, out in zip(X_train[0:2], y_train[0:2]):\n    print(inp,'--', out)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:10:13.817949Z","iopub.execute_input":"2021-11-06T16:10:13.818594Z","iopub.status.idle":"2021-11-06T16:10:13.829951Z","shell.execute_reply.started":"2021-11-06T16:10:13.818551Z","shell.execute_reply":"2021-11-06T16:10:13.829099Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Defining Input shapes for LSTM\nTimeSteps=X_train.shape[1]\nTotalFeatures=X_train.shape[2]\nprint(\"Number of TimeSteps:\", TimeSteps)\nprint(\"Number of Features:\", TotalFeatures)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:10:13.831232Z","iopub.execute_input":"2021-11-06T16:10:13.831559Z","iopub.status.idle":"2021-11-06T16:10:13.840212Z","shell.execute_reply.started":"2021-11-06T16:10:13.831518Z","shell.execute_reply":"2021-11-06T16:10:13.839332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### some of the important hyperparameters of LSTM \n\n1. **units=15**: This means we are creating a layer with fifteen neurons in it. Each of these fifteen neurons will be receiving the values of inputs.\n1. **input_shape = (TimeSteps, TotalFeatures):** The input expected by LSTM is in 3D format. Our training data has a shape of (24470, 30, 1) this is in the form of (number of samples, time steps, number of features). This means we have 25000 examples to learn in training data, each example looks back 30-steps in time like what was the asset price yesterday, the day before yesterday so on till last 30 days. This is known as Time steps. The last number ‘1’ represents the number of features. Here we are using just one column ‘Target’ hence its equal to ‘1’\n1. **kernel_initializer=’uniform’:** When the Neurons start their computation, some algorithm has to decide the value for each weight. This parameter specifies that. You can choose different values for it like ‘normal’ or ‘glorot_uniform’.\n1. **activation=’relu’:** This specifies the activation function for the calculations inside each neuron. You can choose values like ‘relu’, ‘tanh’, ‘sigmoid’, etc.\n1. **return_sequences=True:** LSTMs backpropagate thru time, hence they return the values of the output from each time step to the next hidden layer. This keeps the expected input of the next hidden layer in the 3D format. This parameter is False for the last hidden layer because now it does not have to return a 3D output to the final Dense layer.\n1. **optimizer=’adam’: ** This parameter helps to find the optimum values of each weight in the neural network. ‘adam’ is one of the most used optimizers’\n1. **batch_size=20:** This specifies how many rows will be passed to the Network.\n\n1. **Epochs=5:**  The LSTM network looks at the full training data 5 times and adjusts its weights.","metadata":{}},{"cell_type":"markdown","source":"# Define LSTM Model","metadata":{}},{"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Activation, Dense, Dropout, LSTM\n \n# Initialising the RNN\nregressor = Sequential()\n \n# Adding the First input hidden layer and the LSTM layer\n# return_sequences = True, means the output of every time step to be shared with hidden next layer\nregressor.add(LSTM(units = 15, activation = 'relu', input_shape = (TimeSteps, TotalFeatures), return_sequences=True))\n \n# Adding the Second Second hidden layer and the LSTM layer\nregressor.add(LSTM(units = 10, activation = 'relu', input_shape = (TimeSteps, TotalFeatures), return_sequences=True))\n \n# Adding the Second Third hidden layer and the LSTM layer\nregressor.add(LSTM(units = 5, activation = 'relu', return_sequences=False ))\n \nregressor.add(Dropout(0.3))\n# Adding the output layer\nregressor.add(Dense(units = 1))\n \n# Compiling the RNN\nregressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n \n##################################################\n \nimport time\n# Measuring the time taken by the model to train\nStartTime=time.time()\n \n# Fitting the RNN to the Training set\nregressor.fit(X_train, y_train, batch_size = 15, epochs = 3)\n \nEndTime=time.time()\nprint(\"## Total Time Taken: \", round((EndTime-StartTime)/60), 'Minutes ##')","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:10:13.841348Z","iopub.execute_input":"2021-11-06T16:10:13.841969Z","iopub.status.idle":"2021-11-06T16:13:53.061177Z","shell.execute_reply.started":"2021-11-06T16:10:13.841935Z","shell.execute_reply":"2021-11-06T16:13:53.060214Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Measuring the accuracy on validation data","metadata":{}},{"cell_type":"code","source":"\n# Making predictions on test data\npredicted_Price = regressor.predict(X_test)\npredicted_Price = DataScaler.inverse_transform(predicted_Price)\n \n# Getting the original price values for testing data\norig=y_test\norig=DataScaler.inverse_transform(y_test)\n \n# Accuracy of the predictions\n#print('Accuracy:', 100 - (100*(abs(orig-predicted_Price)/orig)).mean())\n \n# Visualising the results\nimport matplotlib.pyplot as plt\n \nplt.plot(predicted_Price, color = 'pink', label = 'Predicted Volume')\nplt.plot(orig, color = 'green', label = 'Original Volume')\n \nplt.title('Predicting Ethereum ')\nplt.xlabel(' Date')\nplt.xticks(range(TestingRecords), ethereum.tail(TestingRecords)['Target'])\nplt.ylabel(' Price')\n \nplt.legend()\nfig=plt.gcf()\nfig.set_figwidth(20)\nfig.set_figheight(6)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:13:53.062610Z","iopub.execute_input":"2021-11-06T16:13:53.062838Z","iopub.status.idle":"2021-11-06T16:14:02.003404Z","shell.execute_reply.started":"2021-11-06T16:13:53.062812Z","shell.execute_reply":"2021-11-06T16:14:02.002450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Visualizing the Full Data","metadata":{}},{"cell_type":"code","source":"TrainPredictions=DataScaler.inverse_transform(regressor.predict(X_train))\nTestPredictions=DataScaler.inverse_transform(regressor.predict(X_test))\n \nFullDataPredictions=np.append(TrainPredictions, TestPredictions)\nFullDataOrig=FullData[TimeSteps:]\n \n# plotting the full data\nplt.plot(FullDataPredictions, color = 'magenta', label = 'Predicted Price')\nplt.plot(FullDataOrig , color = 'lightblue', label = 'Original Price')\n \n \nplt.title(' Price Predictions')\nplt.xlabel(' Date')\nplt.ylabel(' Price')\nplt.legend()\nfig=plt.gcf()\nfig.set_figwidth(20)\nfig.set_figheight(8)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:14:02.004665Z","iopub.execute_input":"2021-11-06T16:14:02.004929Z","iopub.status.idle":"2021-11-06T16:14:11.289933Z","shell.execute_reply.started":"2021-11-06T16:14:02.004898Z","shell.execute_reply":"2021-11-06T16:14:11.289013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ethereum[['Target']].tail(51).values.flatten()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:14:11.291247Z","iopub.execute_input":"2021-11-06T16:14:11.291555Z","iopub.status.idle":"2021-11-06T16:14:11.468867Z","shell.execute_reply.started":"2021-11-06T16:14:11.291516Z","shell.execute_reply":"2021-11-06T16:14:11.468037Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# With above model, we just predict one last value for ethereum","metadata":{}},{"cell_type":"code","source":"\n# Last 10 days prices\nLast50Days=np.array([ 0.00045282,  0.00061184,  0.0012775 ,  0.00086473,  0.00072952,\n        0.00110401,  0.00171625,  0.00118262,  0.00100348,  0.00164157,\n        0.0029463 ,  0.00314749,  0.00281155,  0.00217294,  0.00238924,\n        0.00390146,  0.00361306,  0.0040886 ,  0.00405105,  0.00269954,\n        0.00242405,  0.00102109,  0.00140944,  0.00131124,  0.00059185,\n       -0.00050834,  0.0010392 ,  0.00123086,  0.00070894,  0.00061493,\n       -0.00096887, -0.00127859, -0.00162916, -0.00223221, -0.00142235,\n       -0.00225987, -0.00152843, -0.00087532, -0.00061999,  0.00025487,\n        0.00057095, -0.00130277, -0.00127819, -0.00129005, -0.00076483,\n       -0.00189372, -0.00130851, -0.0007357 , -0.00049116, -0.00056185\n      ])\n \n# Normalizing the data just like we did for training the model\nLast50Days=DataScaler.transform(Last50Days.reshape(-1,1))\n \n# Changing the shape of the data to 3D\n# Choosing TimeSteps as 10 because we have used the same for training\nNumSamples=1\nTimeSteps=50\nNumFeatures=1\nLast50Days=Last50Days.reshape(NumSamples,TimeSteps,NumFeatures)\n \n#############################\n \n# Making predictions on data\npredicted_Price = regressor.predict(Last50Days)\npredicted_Price = DataScaler.inverse_transform(predicted_Price)\npredicted_Price","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:14:11.470225Z","iopub.execute_input":"2021-11-06T16:14:11.470456Z","iopub.status.idle":"2021-11-06T16:14:12.080776Z","shell.execute_reply.started":"2021-11-06T16:14:11.470417Z","shell.execute_reply":"2021-11-06T16:14:12.080087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Model has predicted -0.00062102 whereas actual target value is -0.0003459, the prediction is not accurate, But, we can improve the model performance....**","metadata":{}},{"cell_type":"markdown","source":"# MultiStep Time Series","metadata":{}},{"cell_type":"markdown","source":"**Assume We need to predict next 15 days price from the last 50 days values**","metadata":{}},{"cell_type":"markdown","source":"# Data Preparation for Multi Step LSTM","metadata":{}},{"cell_type":"code","source":"\n# Considering the Full Data again which we extracted above\n# Printing the last 10 values\nprint('Original Prices')\nprint(FullData[-10:])\n \nprint('###################')\n \n# Printing last 10 values of the scaled data which we have created above for the last model\n# Here I am changing the shape of the data to one dimensional array because\n# for Multi step data preparation we need to X input in this fashion\nX=X.reshape(X.shape[0],)\nprint('Scaled Prices')\nprint(X[-10:])","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:16:40.354876Z","iopub.execute_input":"2021-11-06T16:16:40.355655Z","iopub.status.idle":"2021-11-06T16:16:40.362175Z","shell.execute_reply.started":"2021-11-06T16:16:40.355615Z","shell.execute_reply":"2021-11-06T16:16:40.361524Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Multi step data preparation\n \n# split into samples\nX_samples = list()\ny_samples = list()\n \nNumerOfRows = len(X)\nTimeSteps=50  # next few day's Price Prediction is based on last how many past day's prices\nFutureTimeSteps=15 # How many days in future you want to predict the prices\n \n# Iterate thru the values to create combinations\nfor i in range(TimeSteps , NumerOfRows-FutureTimeSteps , 1):\n    x_sample = X[i-TimeSteps:i]\n    y_sample = X[i:i+FutureTimeSteps]\n    X_samples.append(x_sample)\n    y_samples.append(y_sample)\n    \n# Reshape the Input as a 3D (samples, Time Steps, Features)\nX_data=np.array(X_samples)\nX_data=X_data.reshape(X_data.shape[0],X_data.shape[1], 1)\nprint('### Input Data Shape ###') \nprint(X_data.shape)\n \n# We do not reshape y as a 3D data  as it is supposed to be a single column only\ny_data=np.array(y_samples)\nprint('### Output Data Shape ###') \nprint(y_data.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:16:43.456335Z","iopub.execute_input":"2021-11-06T16:16:43.456960Z","iopub.status.idle":"2021-11-06T16:16:43.672128Z","shell.execute_reply.started":"2021-11-06T16:16:43.456923Z","shell.execute_reply":"2021-11-06T16:16:43.671473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note **FutureTimeSteps** will be the number of days prices to be predicted","metadata":{}},{"cell_type":"markdown","source":"# Train Test Split","metadata":{}},{"cell_type":"code","source":"\n# Choosing the number of testing data records\nTestingRecords=5\n \n# Splitting the data into train and test\nX_train=X_data[:-TestingRecords]\nX_test=X_data[-TestingRecords:]\ny_train=y_data[:-TestingRecords]\ny_test=y_data[-TestingRecords:]\n \n#############################################\n# Printing the shape of training and testing\nprint('\\n#### Training Data shape ####')\nprint(X_train.shape)\nprint(y_train.shape)\n \nprint('\\n#### Testing Data shape ####')\nprint(X_test.shape)\nprint(y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:16:47.859246Z","iopub.execute_input":"2021-11-06T16:16:47.859715Z","iopub.status.idle":"2021-11-06T16:16:47.867295Z","shell.execute_reply.started":"2021-11-06T16:16:47.859665Z","shell.execute_reply":"2021-11-06T16:16:47.866676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Visualizing the input->output sent to LSTM Multi-step model","metadata":{}},{"cell_type":"code","source":"\n# Visualizing the input and output being sent to the LSTM model\n# Based on last 10 days prices we are learning the next 5 days of prices\nfor inp, out in zip(X_train[0:2], y_train[0:2]):\n    print(inp)\n    print('====>')\n    print(out)\n    print('#'*20)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:16:52.308006Z","iopub.execute_input":"2021-11-06T16:16:52.308476Z","iopub.status.idle":"2021-11-06T16:16:52.316418Z","shell.execute_reply.started":"2021-11-06T16:16:52.308416Z","shell.execute_reply":"2021-11-06T16:16:52.315589Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Defining Input shapes for LSTM\nTimeSteps=X_train.shape[1]\nTotalFeatures=X_train.shape[2]\nprint(\"Number of TimeSteps:\", TimeSteps)\nprint(\"Number of Features:\", TotalFeatures)","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:16:58.312490Z","iopub.execute_input":"2021-11-06T16:16:58.313577Z","iopub.status.idle":"2021-11-06T16:16:58.319172Z","shell.execute_reply.started":"2021-11-06T16:16:58.313536Z","shell.execute_reply":"2021-11-06T16:16:58.318600Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Define Multi Step LSTM model","metadata":{}},{"cell_type":"code","source":"# Initialising the RNN\nregressor = Sequential()\n \n# Adding the First input hidden layer and the LSTM layer\n# return_sequences = True, means the output of every time step to be shared with hidden next layer\nregressor.add(LSTM(units = 10, activation = 'relu', input_shape = (TimeSteps, TotalFeatures), return_sequences=True))\n \n \n# Adding the Second hidden layer and the LSTM layer\nregressor.add(LSTM(units = 5, activation = 'relu', input_shape = (TimeSteps, TotalFeatures), return_sequences=True))\n \n# Adding the Third hidden layer and the LSTM layer\nregressor.add(LSTM(units = 5, activation = 'relu', return_sequences=False ))\n \nregressor.add(Dropout(0.25))\n# Adding the output layer\n# Notice the number of neurons in the dense layer is now the number of future time steps \n# Based on the number of future days we want to predict\nregressor.add(Dense(units = FutureTimeSteps))\n \n# Compiling the RNN\nregressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n \n###################################################################\n \nimport time\n# Measuring the time taken by the model to train\nStartTime=time.time()\n \n# Fitting the RNN to the Training set\nregressor.fit(X_train, y_train, batch_size = 15, epochs = 3)\n \nEndTime=time.time()\nprint(\"############### Total Time Taken: \", round((EndTime-StartTime)/60), 'Minutes #############')\n","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:17:03.113551Z","iopub.execute_input":"2021-11-06T16:17:03.113899Z","iopub.status.idle":"2021-11-06T16:27:31.251168Z","shell.execute_reply.started":"2021-11-06T16:17:03.113866Z","shell.execute_reply":"2021-11-06T16:27:31.250379Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Measuring the Accuracy of the model on validation data","metadata":{}},{"cell_type":"code","source":"#Making predictions on test data\npredicted_Price = regressor.predict(X_test)\npredicted_Price = DataScaler.inverse_transform(predicted_Price)\nprint('#### Predicted Prices ####')\nprint(predicted_Price)\n \n# Getting the original price values for testing data\norig=y_test\norig=DataScaler.inverse_transform(y_test)\nprint('\\n#### Original Prices ####')\nprint(orig)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-06T16:28:31.035113Z","iopub.execute_input":"2021-11-06T16:28:31.035406Z","iopub.status.idle":"2021-11-06T16:28:31.667498Z","shell.execute_reply.started":"2021-11-06T16:28:31.035376Z","shell.execute_reply":"2021-11-06T16:28:31.666612Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" \nfor i in range(len(orig)):\n    Prediction=predicted_Price[i]\n    Original=orig[i]\n     \n    # Visualising the results\n    plt.plot(Prediction, color = 'brown', label = 'Predicted Volume')\n    plt.plot(Original, color = 'lightblue', label = 'Original Volume')\n \n    plt.title('### Accuracy of the predictions:'+ str(100 - (100*(abs(Original-Prediction)/Original)).mean().round(2))+'% ###')\n    plt.xlabel(' Date')\n    \n    startDateIndex=(FutureTimeSteps*TestingRecords)-FutureTimeSteps*(i+1)\n    endDateIndex=(FutureTimeSteps*TestingRecords)-FutureTimeSteps*(i+1) + FutureTimeSteps\n    TotalRows=ethereum.shape[0]\n \n    plt.xticks(range(FutureTimeSteps), ethereum.iloc[TotalRows-endDateIndex : TotalRows-(startDateIndex) , :]['Target'])\n    plt.ylabel(' Price')\n \n    plt.legend()\n    fig=plt.gcf()\n    fig.set_figwidth(20)\n    fig.set_figheight(3)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:42:04.579477Z","iopub.execute_input":"2021-11-06T16:42:04.579755Z","iopub.status.idle":"2021-11-06T16:42:06.129332Z","shell.execute_reply.started":"2021-11-06T16:42:04.579726Z","shell.execute_reply":"2021-11-06T16:42:06.128660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Making Prediction for next 15 days","metadata":{}},{"cell_type":"code","source":"ethereum[['Target']].tail(65).values.flatten()","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:42:12.696094Z","iopub.execute_input":"2021-11-06T16:42:12.696857Z","iopub.status.idle":"2021-11-06T16:42:12.713911Z","shell.execute_reply.started":"2021-11-06T16:42:12.696805Z","shell.execute_reply":"2021-11-06T16:42:12.713206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Making predictions on test data\nLast50DaysPrices=np.array([-0.00480228, -0.0048188 , -0.00373259, -0.00228633, -0.00241039,\n       -0.00222162, -0.00076806, -0.00089731, -0.00111598, -0.00050295,\n       -0.00094476, -0.00030015,  0.00144354,  0.00243018,  0.00045282,\n        0.00061184,  0.0012775 ,  0.00086473,  0.00072952,  0.00110401,\n        0.00171625,  0.00118262,  0.00100348,  0.00164157,  0.0029463 ,\n        0.00314749,  0.00281155,  0.00217294,  0.00238924,  0.00390146,\n        0.00361306,  0.0040886 ,  0.00405105,  0.00269954,  0.00242405,\n        0.00102109,  0.00140944,  0.00131124,  0.00059185, -0.00050834,\n        0.0010392 ,  0.00123086,  0.00070894,  0.00061493, -0.00096887,\n       -0.00127859, -0.00162916, -0.00223221, -0.00142235, -0.00225987])\n \n# Reshaping the data to (-1,1 )because its a single entry\nLast50DaysPrices=Last50DaysPrices.reshape(-1, 1)\n \n# Scaling the data on the same level on which model was trained\nX_test=DataScaler.transform(Last50DaysPrices)\n \nNumberofSamples=1\nTimeSteps=X_test.shape[0]\nNumberofFeatures=X_test.shape[1]\n# Reshaping the data as 3D input\nX_test=X_test.reshape(NumberofSamples,TimeSteps,NumberofFeatures)\n \n# Generating the predictions for next 5 days\nNext15DaysPrice = regressor.predict(X_test)\n \n# Generating the prices in original scale\nNext15DaysPrice = DataScaler.inverse_transform(Next15DaysPrice)\nNext15DaysPrice","metadata":{"execution":{"iopub.status.busy":"2021-11-06T16:42:15.774350Z","iopub.execute_input":"2021-11-06T16:42:15.774832Z","iopub.status.idle":"2021-11-06T16:42:15.850605Z","shell.execute_reply.started":"2021-11-06T16:42:15.774785Z","shell.execute_reply":"2021-11-06T16:42:15.849365Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I have taken single asset *Ethereum* here for demo purpose, the competition requires all 14 assets to be predicted..\n\nThank you for reading","metadata":{}},{"cell_type":"markdown","source":"**Few graph ideas are taken from the below beautiful kernels.**\n\nhttps://www.kaggle.com/robikscube/predict-crypto-prices-twitch-stream\n\nhttps://www.kaggle.com/carlmcbrideellis/g-research-plot-financial-data-using-mplfinance/notebook\n\nhttps://www.kaggle.com/sami116/first-steps-crypto-forecasting/notebook","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}