Algorithmic Trading, Machine Learning and Risk Management.

  • Kalman Filter :: Applications in Finance

    Noise in data refers to any unwanted or irrelevant information that interferes with the transmission or processing of a signal. In signal processing, the goal is to extract the useful information (the signal) from the noise. This can be particularly challenging in scenarios like wireless communication, where signals are transmitted through the air and can be distorted or contaminated by various environmental factors. Techniques such as filtering and noise reduction are used to mitigate the effects of noise and recover the

  • A Money Manager's Best Friends

    Competing in the ever-evolving arena of financial markets necessitates not only strategic acumen but also technological prowess. For financial managers, securing a competitive edge involves harnessing reliable machine learning solutions that empower them to implement and manage strategies

  • Navigate to Higher Alpha

    In the fast-paced and competitive landscape of financial investments, the pursuit of higher Alpha , the measure of an investment’s performance relative to the market—is the ultimate goal for investors. Traditional investment strategies often struggle to consistently achieve high Alpha in today’s dynamic markets. However, with the advent of machine learning techniques, investors now have powerful tools at their disposal to uncover hidden patterns, exploit market inefficiencies, and generate alpha with greater consistency and precision.

  • Liquidity Risk Management and EWI

    One of the paramount advantages of contemporary financial risk management lies in its remarkable objectivity. It adopts a scientific methodology, leveraging mathematics and statistics to quantify and assess financial instruments and investment portfolios. Although these mathematical

  • Backtesting Bias

    In an optimal trading environment devoid of backtesting bias, traders would possess a figurative “crystal ball” providing deep insights into the complex dynamics of financial markets, enabling accurate predictions of

  • Winning with Quantitative Trading

    Quantitative Algorithmic trading is the computerised execution of the financial instruments following the trading strategies that are developed using advanced mathematical models. Quantitative methods are used for carrying out the research, analysing the historical data and using the complex mathematical and statistical models to find the trading opportunities that are

  • Quantum Supremacy

    The use of of scalable machine learning and analytics systems across the mostcritical government as well as commercial institutions in the world to solvesome of the hardest (or not, depending on how you see it) analytical problemsof our times can be more easily seen now a days.

  • UK’s Fiscal Tightening and its Burdensome Impact

    A forecast of the British economy in the short to medium term, by anyone’s standards, would be bleak. The inflation, energy crisis, and Brexit have placed the country’s economy in an unenviable position. Last week, the government announced new measures to fill the budget deficit with a £55 billion fiscal squeeze as the inflation reached a 41-year high at 11.1 percent.

  • Polarized Fractal Efficiency

    In finance, one of the key tools gaining traction among analysts and traders alike is the Polarized Fractal Efficiency (PFE) indicator. Developed by Hans Hannula, this indicator has found its way into many financial models and is increasingly relied upon for stock price predictions and market trend analysis.

  • Robo Advisors – Rise or Demise?

    Wealth management has typically only been for those with higher-than-average income, with fees for these services being prohibitive for the general population.

    However, wealth management is becoming increasingly accessible – companies using robo-advisers now provide similar services for a fee of less than 1% of the money being managed. In recent years, the ‘robo-advisers’ have grown rapidly, mainly due to their low-cost offering, technology adoption and ease of access toa wider range of investors. This low-cost model has allowed investors with even smaller amounts to be able to use such services compared to the traditional advisors where large minimum investment was required.