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https://github.com/kennethreitz/coinbin.org.git
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85 lines
2.1 KiB
Python
85 lines
2.1 KiB
Python
import time
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import uuid
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import records
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import os
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import maya
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import numpy as np
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import pandas as pd
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# Matplotlib hack.
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import matplotlib
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matplotlib.use('agg')
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import mpld3
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from fbprophet import Prophet
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from scraper import Coin, MWT, convert_to_decimal
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PERIODS = 30
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GRAPH_PERIODS = 365
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@MWT(timeout=300)
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def get_predictions(coin, render=False):
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"""Returns a list of predictions, unless render is True.
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Otherwise, returns the path of a rendered image.
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"""
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c = Coin(coin)
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q = "SELECT date as ds, value as y from api_coin WHERE name=:coin"
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db = records.Database()
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rows = db.query(q, coin=c.name)
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df = rows.export('df')
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df['y_orig'] = df['y'] # to save a copy of the original data..you'll see why shortly.
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# log-transform y
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df['y'] = np.log(df['y'])
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model = Prophet(weekly_seasonality=True, yearly_seasonality=True)
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model.fit(df)
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periods = PERIODS if not render else GRAPH_PERIODS
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future_data = model.make_future_dataframe(periods=periods, freq='d')
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forecast_data = model.predict(future_data)
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if render:
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matplotlib.pyplot.gcf()
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fig = model.plot(forecast_data, xlabel='Date', ylabel='log($)')
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return mpld3.fig_to_html(fig)
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forecast_data_orig = forecast_data # make sure we save the original forecast data
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forecast_data_orig['yhat'] = np.exp(forecast_data_orig['yhat'])
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forecast_data_orig['yhat_lower'] = np.exp(forecast_data_orig['yhat_lower'])
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forecast_data_orig['yhat_upper'] = np.exp(forecast_data_orig['yhat_upper'])
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df['y_log'] = df['y'] #copy the log-transformed data to another column
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df['y'] = df['y_orig'] #copy the original data to 'y'
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# print(forecast_data_orig)
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d = forecast_data_orig['yhat'].to_dict()
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predictions = []
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for i, k in enumerate(list(d.keys())[-PERIODS:]):
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w = maya.when(f'{i+1} days from now')
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predictions.append({
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'when': w.slang_time(),
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'timestamp': w.iso8601(),
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'usd': convert_to_decimal(d[k]),
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})
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return predictions
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if __name__ == '__main__':
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print(get_predictions('btc'))
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