RMSE = 0.02548
I’m only slightly familiar with support vector machines, although I understand the general principal. Using this article as a guide I ran the algorithm on my data and got the above result. Here’s the code
import numpy as np
import pandas as pd
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
df = pd.read_csv('data/btc.csv', usecols=['date', 'close'], index_col='date', parse_dates=True)
df_returns = df['close'].to_frame().pct_change()
df_returns.rename(columns={'close': 't'}, inplace=True)
df_returns.insert(0, 't-1', df_returns['t'].shift(1))
df_returns.dropna(inplace=True)
X = df_returns['t-1'].to_numpy(dtype=np.float32).reshape(-1, 1)
y = df_returns['t'].to_numpy(dtype=np.float32)
test_limit = 700
X_train, X_test = X[:test_limit], X[test_limit:]
y_train, y_test = y[:test_limit], y[test_limit:]
svr = SVR(kernel='linear')
svr.fit(X_train, y_train)
y_pred = svr.predict(X_test)
loss = mean_squared_error(y_test, y_pred)
print(np.sqrt(loss))I suppose it would be best to summarise the results so far in a separate post.