end-to-end_simple.md

Simple End-to-end Example

0. Introduction

This example predicts future ETH price, using simple input data (just historical ETH price) and a simple model (linear dynamical model).

Predictions are 5mins, 10mins, ..., 60mins into the future.

1. Setup

We assume you've already done main5.md "Setup".

2. Get data locally

In the Python console:

import ccxt
cex_x = ccxt.binance().fetch_ohlcv('ETH/USDT', '5m')
allcex_uts = [xi[0]/1000 for xi in cex_x] # timestamps
allcex_vals = [xi[4] for xi in cex_x] # ETH prices

# # Extracts dates and ether price values
print_datetime_info("CEX data info", allcex_uts)

# Transform timestamps to dates
dts = to_datetimes(allcex_uts)

# create a Data Frame with two columns [date,eth-prices] with dates given in intervals of 5-minutes
import pandas as pd
data = pd.DataFrame({"ds": dts, "y": allcex_vals})

3. Make predictions

3.1 Build an AI model

In the same Python console:

# use the last 12 intervals of 5mins each of testing set, all the previous data is used as training
train_data = data.iloc[0:-12,:]
test_data = data.iloc[-12:,:]

# fit a linear model (Open sourced Facebook's Prophet model: https://facebook.github.io/prophet/)
# As the data is subdaily, the model will fit daily seasonality
from prophet import Prophet
model = Prophet()
model.fit(train_data)

3.2 Run the AI model to make future ETH price predictions

In the same Python console:

#Predict ETH values over the range of the test set
forecast = model.predict(pd.DataFrame({"ds":test_data.ds}))
pred_vals_test = forecast.set_index('ds')['yhat'][-12:].to_numpy()

3.3 Calculate NMSE and make final predictions

In the same Python console:

# now, we have predicted and actual values. Let's find error, and plot!
cex_vals = test_data.y
nmse = calc_nmse(cex_vals, pred_vals_test)
print(f"NMSE = {nmse}")
plot_prices(cex_vals, pred_vals_test)

Keep iterating in step 3 until you're satisfied with accuracy. Then...

# fit model with all the available data
model = Prophet()
model.fit(data)

# generate dates for prediction (12 time perods ahead of the latest datapoint in the data time)
future_dates = model.make_future_dataframe(periods=12, freq="5min", include_history=False)

# predcit eth values on future_dates
forecast = model.predict(future_dates)
pred_vals = forecast.set_index('ds')['yhat'].to_numpy()

4. Publish & share predictions

From Challenge 5, do:

• Publish & share predictions