Enhancing Swing Trading with Accurate Stock Price Forecasting
Swing trading, a strategy focused on capturing short-term price movements, demands quick and informed decisions. However, many traders struggle with the lack of reliable tools that offer precise short-term forecasts. This project aimed to bridge this gap by developing a robust, data-driven forecasting application.
Problem
Swing traders often rely on traditional stock analysis tools that may not provide the granularity or timeliness needed for effective trading. The challenge lies in accurately predicting short-term stock price movements to identify optimal entry and exit points.
Solution
To address this, I developed a web application leveraging a custom-tuned Prophet forecasting model. The model incorporates several key features:
Custom-Tuned Prophet Model:
I fine-tune models using techniques such as hyperparameter optimization, which allows me to achieve the best performance by systematically adjusting model parameters. Additionally, I apply volatility-adjusted winsorization to deal with outliers more effectively. This combination enhances accuracy and ensures robust results in my analyses.
Dynamic Training Data:
The model dynamically adjusts its training data in response to the stock's volatility. This approach enables me to ensure that it learns from relevant historical patterns. Each adjustment is made with the intention of capturing the nuances of market behavior, ultimately enhancing the model's performance.
Winsorization Strategy:
I implemented a winsorization strategy that was tailored based on the volatility of each ticker. By doing this, I was able to effectively reduce the impact of outliers on the dataset. This adjustment not only enhanced the accuracy of my model but also ensured that the model remained robust without succumbing to overfitting.
Visual Aids:
I integrated candlestick charts, Bollinger Bands, and Simple Moving Averages (SMAs) to provide traders with comprehensive visual insights. With candlestick charts, I can visually represent price movements over time, offering clear indications of market trends and reversals. By incorporating Bollinger Bands, I can delineate volatility and potential price extremes, allowing me to make informed trading decisions. Additionally, using SMAs helps me smooth out price data to identify the underlying trend, further refining my analysis. This combination of tools not only enhances my ability to interpret market signals but also supports more strategic trading approaches.
SMAPE Evaluation:
I utilized SMAPE as the primary evaluation metric because of its scale independence and symmetry. By focusing on this metric, I was able to ensure that the evaluation of forecasts remained consistent, regardless of the magnitude of the values. This choice allowed me to effectively compare different models and assess their performance more reliably.
Statistical Validation:
I performed a Wilcoxon signed-rank test to assess the model's statistical significance. Subsequently, I calculated Cliff's Delta to determine the effect size. This quantitative analysis helped me to better understand the model's performance and the practical implications of its findings.
Methodology
This project involved:
Data collection from reliable financial APIs (Finnhub and yfinance).
Data preprocessing, including feature engineering and winsorization.
Model training and validation using time-series cross-validation.
Hyperparameter tuning via grid search to optimize model performance.
Deployment of the application using Streamlit for an interactive user experience.
Results & Findings
The custom-tuned Prophet model demonstrated a median SMAPE of 15% across a sample of 150 diverse stock tickers, showcasing its robust predictive capability.
Compared to the standard Prophet model, the tuned model exhibited a statistically significant improvement (p < 0.05) and a large effect size (Cliff's Delta = 0.69).
The volatility-adjusted winsorization strategy effectively mitigated the impact of outliers, particularly in high-volatility scenarios.
The model's reduced standard deviation of prediction errors (50% vs. 60%) compared to the standard model highlights its increased consistency.
The application provides swing traders with accurate and timely forecasts, along with valuable visual aids to support their trading decisions.
Conclusion
I developed this forecasting tool to significantly enhance my swing trading strategies. By custom-tuning the Prophet model, I validated its performance, demonstrating its potential to equip traders with the information necessary for making informed and timely trading decisions.