Welcome to My Trading Application!

— Analyze and test technical indicators —


About the Trading Application

This Flask-based trading application is designed as a personal project aimed at honing skills in both computer science and financial markets, with a specific focus on backtesting stock strategies. The platform emphasizes not just the use of technical indicators but also the critical process of backtesting to evaluate the effectiveness of trading strategies before deploying them in live market conditions.

RSI Analysis

Perform advanced RSI backtesting on your selected stocks. Just input the stocks you want to analyze and let the system do the rest.

Go to RSI Strategy Go to RSI Strategy (custom parameters)

SMA Analysis

Simple Moving Average (SMA) is a great tool to identify the direction of a stock's price trend. Get started with our SMA analysis.

Go to SMA Analysis Go to SMA Strategy (custom parameters)

Market Overview

Market Overview

  Stock Last Close Price YTD % Change
0 AAPL 254.49 37.09
1 MSFT 436.60 17.72
2 GOOGL 191.41 38.53
3 AMZN 224.92 50.02
4 TSLA 421.06 69.50
5 V 317.71 22.73
6 NVDA 134.70 -72.04
7 META 585.25 69.01
8 UNH 500.13 -7.27
9 LLY 767.76 29.65
10 JPM 237.60 38.08
11 XOM 105.87 3.43
12 JNJ 144.47 -9.69
13 PG 168.06 12.99

Backtesting Overview

At the core of this platform is the ability to backtest trading strategies using historical market data. While developed to be used on technical data, the source code can easily be modified to apply to all sorts of different trading metrics. The backtesting process involves applying a strategy to historical stock prices to simulate how it would have performed in the past. This allows traders to:

  • Evaluate Strategy Performance: Determine whether a strategy would have been profitable.
  • Identify Weaknesses: Expose scenarios where the strategy might fail, allowing for refinement.
  • Optimize Parameters: Experiment with different settings to maximize returns or minimize risk.

Alpaca API Integration for Backtesting

The application utilizes the Alpaca API to retrieve historical stock data and execute virtual trades during backtests. The API serves several purposes, including:

  • Accurate Data Retrieval: Ensures access to reliable, up-to-date historical data for realistic simulations.
  • Realistic Simulations: Enables simulations of real trading conditions, reflecting market price fluctuations.
  • Simulated Trades: Trades are executed under realistic conditions during backtests, mimicking real stock orders.

Key Ideas

Hypothesis-Driven Development: Every trading strategy is based on a hypothesis (e.g., "RSI below 30 signals oversold conditions"). Backtesting allows the user to validate this hypothesis with historical data, ensuring that strategies are tested before real trading.

Data-Driven Decision Making: The application leverages historical stock data, helping users avoid emotional trading decisions and rely on concrete data instead. This leads to more informed, data-driven strategy refinement.

Iterative Testing and Optimization: Backtesting is an iterative process, allowing users to adjust parameters and combine indicators to improve the robustness of their strategies in various market conditions.

Risk Management: Backtesting also serves to expose risks, helping users develop strategies with built-in risk controls (e.g., setting stop losses or adjusting capital allocation based on historical performance).

My Contact Info

kopenshaw2024@gmail.com
LinkedIn
(949)-633-3389