Algorithmic Trading: Evolution of Algorithmic Trading

Tomer Solel is a Financial Analyst at I Know First. He graduated from Cal Poly Pomona with a bachelor's degree in applied mathematics.
  • The two methods used for algotrading are high frequency and quantitative trading.
  • The steps in understating machine learning include providing framework, giving examples to learn from, fitness function, sequential, and generalization requirement. Everyone wants to report results as accurate as possible and as fast as possible.
  • Genetic algorithm is another type of algorithm. The steps in a genetic algorithm include combination, mutation, crossover, and selection; hence the name “genetic algorithm”.
  • Our algorithm predicts over 3,000 markets in 6 different time horizons for short and long term for stocks, commodities, ETF’s, interest rates, currencies, and world indices.
  • The I Know First daily market heat map includes signal and predictability for different stocks, and recently, in a swing trading report, the I Know First Algorithm Performance crushed the S&P 500’s performance.

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Algorithmic Trading vs Mass Psychology

Tomer Solel is a Financial Analyst at I Know First. He graduated from Cal Poly Pomona with a bachelor's degree in applied mathematics. Algorithmic Trading vs Mass Psychology

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Stock Market Forecast: Creating a Model for Chaos Mapping and Predictions

taliTali Soroker is a Financial Analyst at I Know First.


Summary
  • Chaotic systems revisited – see original article here
  • Understanding the stock market as a chaotic process
  • What is a model and what is a multivariate time series?
  • How is machine learning used to create an efficient/effective model For stock market forecast
  • Considering higher dimension data sets and time complexity

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Stock Market Forecast: Chaos Theory Revealing How the Market Works

I Know First Research | May 8th 2014

How Can We Predict the Financial Markets by Using Algorithms? Common fallacies about markets claim markets are unpredictable. However, chaos theory together with powerful algorithms proves such statements are wrong. Markets are chaotic systems with complex dynamics, yet to a certain extent we can make valid stock market forecasts. Using these forecasts generated by cutting-edge predictive algorithms together with a careful risk management strategy may give a trader a significant competitive advantage.

Markets Are Complex Systems

Looking at the common fallacies about stock markets, we can see two major groups. The first group is connected to the classical economic theory, which claims that markets are 100% efficient, and as such unpredictable. However, trying to make predictions regarding the markets is useless anyway, as no stock can be possibly be a better deal than another. Both of them are efficient and everybody in the market has perfect information available to them. From our daily lives it is obvious that this does not truly reflect reality. There are people who actually profit trading stocks, which should not be possible in this idealistic market of economy theories.

Stock Picking Algorithms: The Machine Advantage

Tomer Solel is a Financial Analyst at I Know First. He graduated from Cal Poly Pomona with a bachelor's degree in applied mathematics. Stock Picking Algorithms

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I Know First Live Forecast Evaluation for Western Markets Based On New Global Volatility Adjusted Approach

Executive Summary

As of December 2018, I Know First finished a performance evaluation of the live AI-based predictive forecasts for the new West stock strategy sent to customers. This evaluation clearly demonstrates the consistent out-performance of I Know First’s forecasts vs. the entirety of stocks within the West Market. The West Market is comprised of stocks in Western countries such as the U.S., UK, Canada, and Brazil. This evaluation is part of our continuous studies of live I Know First’s AI predictive algorithm performance.

West Market Highlights:

  • Selecting the West Market stocks with the top 5 signal adjusted strength provides the highest return consistently beating other filters for the 2-week and 3-month horizons
  • Selecting the All signals indicator yielded the best results for the 3-day time horizon
  • There is a positive correlation between the length of the time horizon and the return as the length increases the accuracy and performance of the I Know First algorithm dramatically improves
  • For the 3-day, 1-week, 2-week, 1-month, and 3-month time horizons all signal filters outperform the benchmark with the top 10 signal on the 3-month horizon providing the largest return of 2.11%

Evaluation Period and Universe

In this Live Forecast Evaluation Report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the West Market stock universe. Our analysis covers the time period from January 1, 2018 – December 1, 2018. We present below the optimal stock picking strategy for this period. This period was chosen since this was a milestone in which I Know First introduced a major new version of the stock picking method for West Market investment universe. It includes analysis of all the predictions generated by the algorithm and sent daily to I Know First’s clients over this time period. The results of this study illustrate the significant positive effects of the algorithm’s continuous improvement utilizing advanced machine learning and AI capabilities.

The asset universe under consideration is the full 10,200 set of assets that I Know First forecasts. These assets include stocks, commodities and currency pairs. Each daily forecast sent to I Know First’s clients includes predictions for the following time horizons: 3 days, 7 days, 14 days, 1 month, 3 month and 1 year.

About the I Know First Algorithm

The I Know First self-learning algorithm analyses, models, and predicts the stock market. The algorithm is based on Artificial Intelligence (AI) and Machine Learning (ML) and incorporates elements of Artificial Neural Networks and Genetic Algorithms.

The system outputs the predicted trend as a number, positive or negative, along with a wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit. Since the model is 100% empirical, the results are based only on factual data, thereby avoiding any biases or emotions that may accompany human derived assumptions. The human factor is only involved in building the mathematical framework and providing the initial set of inputs and outputs to the system. The algorithm produces a forecast with a signal and a predictability indicator. The signal is the number in the middle of the box. The predictability is the number at the bottom of the box. At the top, a specific asset is identified. This format is consistent across all predictions.

Our algorithm provides two independent indicators for each asset – Signal and Predictability.

The Signal is the predicted strength and direction of movement of the asset. Measured from -inf to +inf.

The predictability indicates our confidence in that result. It is a Pearson correlation coefficient between past algorithmic performance and actual market movement. Measured from -1 to 1.

Here is the detailed description of the heatmap.

Our New Stock

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I Know First Live Forecast Evaluation for Eastern Markets Based On New Global Volatility Adjusted Approach

Executive Summary

As of December 2018, I Know First finished a performance evaluation of the live AI-based predictive forecasts for the new East stock strategy sent to customers. This evaluation clearly demonstrates the consistent out-performance of I Know First’s forecasts vs. the entirety of stocks within the East Market. The East Market is comprised of stocks in Eastern countries such as China, Japan, Australia, and New Zealand. This evaluation is part of our continuous studies of live I Know First’s AI predictive algorithm performance.

East Market Highlights:

  • Selecting the East Market stocks with the top 5 signal adjusted strength provides the highest return consistently beating other filters for each time horizon with the exception of the top 10 signal, which provide the highest return for the 3-month time horizon
  • There is a clear correlation between the length of the time horizon and the return as the length increases the accuracy and performance of the I Know First algorithm dramatically improves
  • For the 3-day, 1-week, 2-week, 1-month, and 3-month time horizons all signal filters outperform the benchmark with the top 10 signal on the 3-month horizon providing the largest return of 13.88%

Evaluation Period and Universe

In this Live Forecast Evaluation Report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the East Market stock universe. Our analysis covers the time period from January 1, 2018 – December 1, 2018. We present below the optimal stock picking strategy for this period. This period was chosen since this was a milestone in which I Know First introduced a major new version of the stock picking method for East Market investment universe. It includes analysis of all the predictions generated by the algorithm and sent daily to I Know First’s clients over this time period. The results of this study illustrate the significant positive effects of the algorithm’s continuous improvement utilizing advanced machine learning and AI capabilities.

The asset universe under consideration is the full 10,200 set of assets that I Know First forecasts. These assets include stocks, commodities and currency pairs. Each daily forecast sent to I Know First’s clients includes predictions for the following time horizons: 3 days, 7 days, 14 days, 1 month, 3 month and 1 year.

About the I Know First Algorithm

The I Know First self-learning algorithm analyses, models, and predicts the stock market. The algorithm is based on Artificial Intelligence (AI) and Machine Learning (ML) and incorporates elements of Artificial Neural Networks and Genetic Algorithms.

The system outputs the predicted trend as a number, positive or negative, along with a wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit. Since the model is 100% empirical, the results are based only on factual data, thereby avoiding any biases or emotions that may accompany human derived assumptions. The human factor is only involved in building the mathematical framework and providing the initial set of inputs and outputs to the system. The algorithm produces a forecast with a signal and a predictability indicator. The signal is the number in the middle of the box. The predictability is the number at the bottom of the box. At the top, a specific asset is identified. This format is consistent across all predictions.

Our algorithm provides two independent indicators for each asset – Signal and Predictability.

The Signal is the predicted strength and direction of movement of the asset. Measured from -inf to +inf.

The predictability indicates our confidence in that result. It is a Pearson correlation coefficient between past algorithmic performance and actual market movement. Measured from -1 to 1.

Here is the detailed description of the heatmap.

Our New Stock Picking Method

The new stock picking method takes all 10,200 assets, the global set, that are forecasted by I Know First. The assets are then filtered by predictability and the top 30 most predictable assets are selected. A volatility measurement is then conducted. These top 30 assets are then adjusted for this volatility measurement. The East Market stocks are then selected from this final set of top 30 global most predictable assets with volatility adjusted signal.

As mentioned before, the asset universe under consideration is the full 10,200 set of assets covered by I Know First forecast and includes stocks, commodities and currency pairs. By implementing this new stock picking method, we are selecting only the most predictable assets and the ones with the highest signals. However, there could be occasions where no East Market stocks fall in this final set of 30 assets. If this was the case, a stock selection will not take place on such occasion.

The Performance Evaluation Method

We perform evaluations on the individual forecast level. It means that we calculate what would be the return of each forecast we have issued for each horizon in the testing period. Then, we take the average of those results by strategy and forecast horizon.

For example, to evaluate the performance of our 1-month forecasts, we calculate the return of each trade by using this formula:


This simulates a client purchasing the asset based on our prediction and selling it exactly 1 month in the future.

We iterate this calculation for all trading days in the analyzed period and average the results.

Note that this evaluation does not take a set portfolio and follow it. This is a different evaluation method at the individual forecast level.

The Benchmarking Method

The theory behind our benchmarking method is the “Null hypothesis“, meaning buying every stock in the particular asset universe regardless of our I Know First indicators.

In comparison, only when our signals are of high signal strength and high predictability for a specific stock, then it should be bought (or shorted).

The ratio of our signal’s trading results to benchmark results indicates the quality of the system and our indicators.

Example: A benchmark for the 3 days horizon means buy on each day and sell exactly 3 business days afterwards. We then average the results to get the benchmark. This is to get an “apples to apples” comparison.

Performance

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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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