A tool for securities analysis and trading strategy back-testing

The platform created in this project will help users to analyze stock/ETF performance, define and improve their asset allocation plans and trade ideas. Using real market data, user can do a deep dive into securities performance, historical correlation, risk and return. Furthermore, user can use the platform back test their trade ideas, modify the parameters, including percentage allocation, risk limits and rebalancing frequency, and create the best strategy which suits their risk/return criteria and long-term wealth management goals

Optimising the smoothness and accuracy of moving average for stock price data

Smoothing time series allows removing noise. Moving averages are used in finance to smooth stock price series and forecast trend direction. We propose optimised custom moving average that is the most suitable for stock time series smoothing. Suitability criteria are defined by smoothness and accuracy. Previous research focused only on one of the two criteria in isolation. We define this as multi-criteria Pareto optimisation problem and compare the proposed method to the five most popular moving average methods on synthetic and real world stock data. The comparison was performed using unseen data. The new method outperforms other methods in 99.5% of cases on synthetic and in 91% on real world data. The method allows better time series smoothing with the same level of accuracy as traditional methods, or better accuracy with the same smoothness. Weights optimised on one stock are very similar to weights optimised for any other stock and can be used interchangeably. Traders can use the new method to detect trends earlier and increase the profitability of their strategies. The concept is also applicable to sensors, weather forecasting, and traffic prediction where both the smoothness and accuracy of the filtered signal are important

Simulated Complete Multiple Hedging Programs Employing Optimized Moving Average Combinations for Use by Continuously Operated Feedlots

Agricultural Economic

Technical Marketing Strategy Using Weekly Average Cash Prices of Slaughter Cattle for Use by Continuous Feedlots

Agricultural Economic

Simulation of Short and Long Feeder Cattle Hedging Strategies and Technical Price Analysis of the Feeder Cattle Futures Market

Agricultural Economic