Interactive Stock Price Movement Probabilities with Python
Dynamic Price Movement Probability
1. Introduction
When diving into the financial world, understanding stock price movements is a significant advantage. Here, we present an interactive method that offers insights into the likelihood of stock prices hitting certain targets, based on historical data.
Leveraging Python’s powerful libraries, this tool is both informative and visually captivating. The presented tool doesn’t predict future prices but assesses the historical frequency of price swings, letting you visualize how often, historically, a stock hit certain price changes over a specified period.
2. Behind the Scenes: Libraries and Data
Libraries Used:
- yfinance: Our data genie that fetches historical stock data.
- pandas & numpy: The backbone for data wrangling and calculations.
- seaborn & matplotlib: For turning raw numbers into insightful visualizations.
- ipywidgets: The magic wand that adds interactivity to our visualizations.
Fetching the Data
Our default stock for illustration is VOW.DE, representing Volkswagen AG. The time frame chosen spans from 2000 to 2023
3. Functionality Overview
Price Movement Probability Computation
The methodology is rooted in historical data analysis. The primary steps include:
- Computing the percentage change over a specified number of days, termedÂ
n_days. - Assessing the frequency of these changes that surpass or descend below user-specified thresholds.
- Representing these counts as historical probabilities.
Parameter Specification
Users are facilitated to modify several parameters:
- The duration (
n_days) for which the price change is evaluated. - An initial reference price.
- Upward (
up_target) and downward (down_target) price thresholds.
Such customizations enable varied analytical perspectives based on differing scenarios.
Figure 1. Interactive sliders in action: Tailoring the analysis parameters to delve deeper into stock price movement patterns
Core Logic for Computing Price Movement Probabilities
At the heart of this tool lies the logic to compute the probabilities of stock price movements:
# Calculate the thresholds for price change
up_threshold = round(abs((up_target - initial_price) / initial_price), 3)
...
up_frequency = up_periods / total_periods
Here, the tool:
- Determines the percentage thresholds for desired price increases or decreases.
- Computes the percentage change over a user-specified number of days (
n_days). - Calculates the historical frequency at which these thresholds have been crossed.
Visual Representations
The visual component is twofold:
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(24, 8))
...
plt.show()
- The top panel displays the distribution of the percentage changes and highlights when thresholds are crossed.
- The bottom panel tracks the stock’s historical price, pinpointing moments it surpassed set thresholds.
4. User Interactivity Through ipywidgets
The tool’s charm lies in its interactivity:
n_days_slider = widgets.IntSlider(...)
...
interact(estimate_probability, n_days=n_days_slider, ...);
Here, ipywidgets empowers users to adjust parameters, such as the target price or number of days. The interact function ties these interactive elements to the core logic, enabling users to receive instant visual feedback as they modify inputs.
5. Putting It All Together: The Complete Code
For readers eager to have a comprehensive view or run the tool themselves, here’s the full code assembled in one place:
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