Day Trading Strategy: An In-depth Analysis of Realistic Back-Tests

Daniel Tal is a Quantitative Analyst at I Know First. He is currently a candidate for his bachelor's degree in Computer Science and Business Management at Columbia University.

  • Implementation of IKF strategy in intraday trading environment
  • Quantopian slippage and commissions models used to simulate real-time trading
  • Data and statistical Analysis of the methods used to gain day trading returns
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Artificial Intelligence Stock Market: Algorithmic Analysis of Humans and Their Behavior

taliTali Soroker is a Financial Analyst at I Know First.
  • Algorithms are being used every day to analyze human behavior and decision-making
  • Companies use these algorithms to reach relevant customers and expand their reach
  • AI could prove to be beneficial to employers seeking a diverse and successful company
  • Using Algorithms to analyze human thought process can help investors make profitable stock trades
  • Companies are using AI and algorithmic systems to analyze human behavior in the stock market to make accurate forecasts of stock trends

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Algorithmic Trading: Machines Account for Upwards of 80% of Trades in the USA

Algorithmic Trading:

“Eighty percent of daily volume in the U.S. is done by machines” - Guy De Blonay, fund manager at Jupiter Asset Management

Summary:

  • New long-term strategies are being built around algorithmic trading and machine trading is now slowly eating further into long term investments.
  • The volume of trades in the USA done by machines can be up to 80% according to recent claims.
  • Blackrock are set to give a number of their financial analysts the boot in favour of a more updated trading strategy that draws on machines and Artificial Intelligence.
  • I Know First’s state of the art algorithm, uses Artificial Intelligence and self-learning capabilities to predict asset price movements in the market today.

Source: Flickr

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Algorithmic Trading With I Know First Versus High Frequency Trading

Algorithmic Trading

Competition between investment firms is more intense than ever before as firms are expected to be able to beat the S&P 500 on a regular basis to retain and attract new investors. The market is evolving beyond previously established theories however investors still expect strong and consistent returns. Traditional tools and fundamental analysis are no longer enough to stay competitive in the contemporary market. Investment firms need to stay one step ahead in order to be the first to recognize trends and take advantage of opportunities. To stay competitive they are looking to employ the most advanced tools to enhance performance. Algorithmic trading is now a growing trend filling this void. Algorithmic “Buy” and “Sell” orders account for 60%-70% of the US equity market volume. Previously, only large investment firms and hedge funds were able to utilize these advanced mathematical models but I Know First: Daily Market Forecast, a financial start-up, has developed an advanced self-learning algorithm that is being employed by professionals and retail investors alike.

Stock Filtering by the I Know First Signal and Predictability Indicators

Dario Biasini is a Research Analyst at I Know First.





Summary
We expand on research performed in previous articles by further exploring the effect and interpretation of the I Know First prediction measures and how these can be used for stock filtering. We show that as predictability and signal strength increase the average trade returns based on these indicators grow in a consistent, significant, and robust manner and that by daily selecting stocks with the highest predictabilities and signals average returns significantly above those of S&P500 Index can be achieved.
  • Analysis of the Returns Generated by Filtering S&P500 Stocks using the I Know First Signal and Predictability Indicators
  • Comparison of the Compounded Returns Generated by using the I Know First Signal and Predictability Indicators against those of the whole S&P500 Stock Universe

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Deep Learning Finance: Revolutionizing The Market Today

This article was written by David Shabotinsky, a Financial Analyst at I Know First, and enrolled at the undergraduate Finance program at the Interdisciplinary Center, Herzliya.

Deep Learning Finance

Summary
  • How Deep Learning developed from AI
  • The evolution of Deep Learning in the market
  • How the finance sector has begun to further take advantage of Deep learning
  • I Know First implementation of Deep Learning to better forecast financial markets

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Understanding Stock Market Prediction Using Artificial Neural Networks and Their Adaptation

taliTali Soroker is a Financial Analyst at I Know First. She graduated from Northeastern University with a Bachelor degree in Mathematics.

Stock Market Prediction Using Artificial Neural Networks

Summary:
  • Artificial Neural Networks
  • General Applications
  • Different Types of ANNs
    • Feed-Forward ANNs
    • RBF Neural Networks
    • MLP Neural Networks
    • RNNs
  • Neural Networks and Finance

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