线性回归股价预测

Machine learning for finance in python

Preparing data and a linear model

Explore the data with some EDA

Any time we begin a machine learning (ML) project, we need to first do some exploratory data analysis (EDA) to familiarize ourselves with the data. This includes things like:raw data plots histograms and more…
I typically begin with raw data plots and histograms. This allows us to understand our data’s distributions. If it’s a normal distribution, we can use things like parametric statistics.（非参数统计）
There are two stocks loaded for you into pandas DataFrames: lng_df and spy_df (LNG and SPY). We’ll use the closing prices and eventually volume as inputs to ML algorithms.

print(lng_df.head())  # examine the DataFrames
print(spy_df.head())  # examine the SPY DataFrame

# Plot the Adj_Close columns for SPY and LNG
spy_df['Adj_Close'].plot(label='SPY', legend=True)
lng_df['Adj_Close'].plot(label='LNG', legend=True, secondary_y=True)
plt.show()  # show the plot
plt.clf()  # clear the plot space

# Histogram of the daily price change percent of Adj_Close for LNG
lng_df['Adj_Close'].pct_change(1).plot.hist(bins=50)
plt.xlabel('adjusted close 1-day percent change')
plt.show()

大致符合正态.日差异比较小.

Correlations

Correlations are nice to check out before building machine learning models, because we can see which features correlate to the target most strongly. Pearson’s correlation coefficient is often used, which only detects linear relationships. It’s commonly assumed our data is normally distributed, which we can “eyeball” from histograms. Highly correlated variables have a Pearson correlation coefficient near 1 (positively correlated) or -1 (negatively correlated). A value near 0 means the two variables are not linearly correlated.
If we use the same time periods for previous price changes and future price changes, we can see if the stock price is mean-reverting (bounces around) or trend-following (goes up if it has been going up recently).