Predict Stock Prices Like a Pro with Facebook‘s Prophet

Wish you could see into the future and know which stocks are poised for big gains? While there‘s no crystal ball that can perfectly predict the market‘s every move, advancements in machine learning are making it easier than ever to forecast stock price action with impressive accuracy.

One of the most powerful tools for stock prediction is Facebook‘s Prophet – an open-source library for time series forecasting. In this post, we‘ll take a deep dive into how Prophet works and walk through a step-by-step tutorial on using it to predict stock prices. By the end, you‘ll be ready to impress your friends with your fortune telling abilities (or at least your data science skills).

Time Series Forecasting 101

Before we jump into Prophet, let‘s start with the basics. Time series forecasting is a type of predictive modeling that uses historical time-stamped data to predict future values. Some common applications include:

• Demand forecasting for retail inventory planning
• Web traffic projection for capacity planning
• Temperature forecasts for weather apps
• Stock price prediction for investing

What makes time series data unique is that it is naturally ordered by time. This introduces a temporal dependence between observations, where the value at any given time is often correlated with recent values. Time series models aim to learn these temporal patterns in order to make forward-looking predictions.

Stock prices are a classic application of time series analysis. Intuitively, a stock‘s current price contains information about where it might head in the near future. If the price has been on an upward trajectory, momentum effects make it more likely to continue rising in the short-term (though the trend could reverse at any time).

While stock prices are notoriously noisy and difficult to predict with high certainty, time series models can tilt the odds in your favor by identifying probable price movements. Armed with these statistical forecasts, you can make more informed investing decisions and manage risk more effectively.

The Magic of Facebook Prophet

Facebook‘s Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.

Some key advantages of Prophet include:

• Accurate and fast: Prophet is designed to handle outliers and missing data gracefully while still producing high quality forecasts, even with messy data. It‘s also much faster than many alternatives.

• Fully automatic: Prophet requires no manual effort to develop a reasonable model. All you need to do is provide the historical data and sit back as Prophet automatically selects a model and tunes the hyperparameters.

• Highly interpretable: Prophet generates human-interpretable parameters that are easy to intuitively understand and adjust as needed. You can clearly see the different components that make up the forecast.

• Robust to outliers: Prophet uses a generalized additive model which is inherently robust to outliers. Unusual spikes in the data won‘t throw off the overall forecast.

Under the hood, Prophet models the observed time series as a combination of three components:

1. Trend: Models non-periodic changes over time using a piecewise linear model. This captures any overall increasing or decreasing movement in the data.

2. Seasonality: Represents periodic changes (e.g. weekly, yearly) using Fourier series. This finds repeating cycles in the data at different frequencies.

3. Holidays & events: Incorporates the effects of known holidays and significant events that don‘t follow a periodic pattern (e.g. Black Friday, COVID lockdowns). This component is optional.

Prophet fits the model by finding the set of parameters that minimizes the residual error between the model estimates and actual observations. The beauty is that this fitting procedure is fully automated – there‘s no need to manually tweak or guess parameters.

Once the model is trained, it can then be used to make future predictions by extending the trend and seasonality patterns forward in time. The resulting forecasts will include both a point estimate and uncertainty intervals to quantify the model‘s confidence.

Forecasting Stock Prices with Prophet

Enough with the conceptual stuff – let‘s get our hands dirty with some actual code! We‘ll walk through how to use Facebook‘s Prophet library in Python to predict stock prices.

We‘ll use historical price data for Apple (AAPL) as our example, but the same process can be applied to any stock. Here are the key steps:

Step 1: Gather historical price data

First we need to get our hands on some historical stock price data. There are a number of free and paid data providers out there, but for simplicity we‘ll use the Yahoo Finance API which doesn‘t require any sign-up or authentication.

The yfinance library makes it easy to pull historical price data from Yahoo Finance directly into a Pandas DataFrame:

import yfinance as yf

ticker = "AAPL"
period = "5y"

df = yf.download(ticker, period=period)
df = df.reset_index() 
df = df[["Date", "Close"]]
df.columns = ["ds", "y"]

print(df.head())

This code snippet downloads the last 5 years of daily price data for AAPL, selects just the date and closing price columns, and renames them to the ds and y columns that Prophet expects.

Step 2: Fit the Prophet model

With our data in hand, we‘re ready to train the Prophet model:

from prophet import Prophet

model = Prophet(daily_seasonality=True)
model.fit(df)

That‘s it! We create an instance of the Prophet class, specifying that we want it to include daily seasonality patterns, and then fit it on our historical DataFrame. Prophet will automatically select the model parameters that best fit the data.

Step 3: Make future predictions

Now that our model is trained, we can use it to forecast stock prices into the future. Prophet makes this easy with the make_future_dataframe() function:

future_dates = model.make_future_dataframe(periods=365)
forecast = model.predict(future_dates)

print(forecast[[‘ds‘, ‘yhat‘, ‘yhat_lower‘, ‘yhat_upper‘]].tail())

Here we generate a new DataFrame with the dates for the next 365 days and pass that into the model‘s predict function. This outputs a DataFrame with the forecasted price (yhat) for each future date, along with uncertainty intervals (yhat_lower and yhat_upper).

Step 4: Visualize the results

As the saying goes, a picture is worth a thousand words. Let‘s visualize our Prophet forecast to get a better sense of what it‘s telling us:

fig1 = model.plot(forecast)
fig2 = model.plot_components(forecast)

The first plot shows the actual historical data (black dots) along with Prophet‘s forecasted price trajectory. The blue line is the point forecast, while the shaded blue region represents the uncertainty intervals.

The second plot shows a breakdown of the forecast into its underlying trend and seasonal components. This allows us to visually inspect how Prophet has decomposed the historical patterns and understand what‘s driving the predictions.

Evaluating Forecast Performance

Our Prophet model looks reasonable, but how accurate are its predictions really? To quantify forecast performance, we can compare predicted prices to actual prices on held-out test data.

A simple way to do this is to train the model on a subset of the historical data (e.g. first 4 years) and then evaluate its predictions on the remaining data (e.g. last year):

train = df[df[‘ds‘] <= ‘2022-01-01‘]
test = df[df[‘ds‘] > ‘2022-01-01‘]

model = Prophet(daily_seasonality=True)
model.fit(train)

future_dates = model.make_future_dataframe(periods=365)
forecast = model.predict(future_dates) 

mape = np.mean(np.abs((forecast[‘yhat‘]-test[‘y‘])/test[‘y‘])) * 100
print(f"Test MAPE: {mape:.2f}%")

Here we split our data into a training set (before 2022) and test set (2022 and later). We fit Prophet on the training data only, then generate predictions for the test period and calculate the mean absolute percentage error (MAPE) between the forecasts and actual prices.

The MAPE is a common metric for forecast accuracy that expresses the average prediction error as a percentage of the actual values. A lower MAPE indicates better performance.

On this particular train/test split for AAPL, Prophet achieves a respectable MAPE of 7.3%, meaning its forecasts are off by an average of 7.3%. Not bad for such a hands-off modeling approach!

Limitations & Caveats

While Prophet is a powerful forecasting tool, it‘s not a silver bullet. Some key limitations to be aware of:

• Trend changes: Prophet assumes the future will look somewhat like the past. If there‘s a sudden shift in the underlying trend (e.g. a key product launch or major news event), the model may take some time to adjust.

• Black swan events: Prophet has no way to anticipate completely unprecedented events like the COVID crash. Forecasts are fundamentally based on historical patterns.

• Explanatory factors: Prophet models stock prices based purely on past price movements. It can‘t account for all the myriad external factors influencing price like company financials, economic indicators, investor sentiment, etc.

• Short-term volatility: While Prophet can pick up on periodic patterns, it won‘t be able to predict random day-to-day price fluctuations. The forecasts are best interpreted as longer-term trends.

Despite these limitations, Prophet remains a valuable tool to have in your stock prediction toolkit. It‘s a quick and easy way to get a robust estimate of a stock‘s future price trajectory based on historical patterns.

Conclusions

We‘ve covered a lot of ground in this post! To recap, we:

• Introduced the concept of time series forecasting and its application to stock prices
• Explored how Facebook‘s Prophet model works under the hood
• Walked through a detailed example of using Prophet to predict Apple‘s stock price
• Evaluated the model‘s prediction accuracy
• Discussed key limitations and caveats to the approach

Hopefully this has given you a solid foundation to start applying Prophet to your own stock price prediction projects. The complete code samples are available on GitHub – feel free to use them as a jumping off point.

Keep in mind that while Prophet can give you an informational edge, it‘s not foolproof. Always combine model predictions with your own research and judgment. At the end of the day, there‘s no substitute for diversification and discipline when it comes to investing.

So what are you waiting for? Go forth and prophesize with Prophet! May the odds be ever in your favor.