Tosan Ofogh’s first step was to develop a Heterogeneous Agent Model (HAM) which was used to analyse company stocks in Tehran Stock Exchnage’s (TSE). This proved an initial success for our first models and attracted some capital around $50,000.During this time, Tosan Ofogh’s main two goals were, risk management and increasing return on stock trading.
2018JulyTosan Ofogh’s Generation I models were upgraded by combining its previous models, statistical arbitrage and mean reversion. This was to utilise its trading strategies, for a step towards becoming a quantitative hedge fund. Tosan Ofogh’s main focus then shifted towards portfolio diversification. In the way of progress, due to a large amount of data, Tosan Ofogh had to concentrate on machine learning techniques, such as PCA to avoid overfitting. Tosan Ofogh portfolio reached an estimated amount of $200,000.
2019JanuaryOne of the most important aspects of investment in stocks is calculations of Momentum. Tosan Ofogh considers Mean Reversion also to be an important first step. So, by combining diverse strategies alongside Fundamental Features, Tosan Ofogh was able to create better and more efficient models.
2019JuneThrough our development models, Tosan Ofogh has figured out that algorithmic and high-frequency trading can establish a more significant model revolution so that there could be more profit gained. It should also be noted that Tosan Ofogh is aware of the effects of news on stock market prices, which is why models are developed in a way that considers these parameters and Order Book Analysis.
Due to the importance of the oil and gas industry in the financial markets, Tosan Ofogh sensed that they needed to develop a more fundamental model for big oil and gas companies, including RDS, BP, Total, Tullow and EnQuest. So, there was a development of fundamental analysis for these oil companies and Tosan Ofogh hired the best oil experts and petroleum engineers to collect data from these companies, in order for their NPV to be calculated. Tosan Ofogh portfolio reached an estimated amount of $1,000,000.
2020JanuaryTosan Ofogh was forecasting companies KPI’s and indices through hiring various oil and gas experts. These measurements helped Tosan Ofogh improve the results of the vision about the real price of a company. By paying close attention to these factors, then calculating a company's present and future cash flow, Tosan Ofogh was able to claim the company's real fundamental price.
2020MarchThe news that is published everyday, whether positive, negative or neutral. Whether it's good for the signal, bad for the signal or has no effect on the signal. Tosan Ofogh used a NLP to understand whether news that has been published was neutral, positive or negative. Tosan Ofogh portfolio reached an estimated amount of $15,000,000.
2021FebruaryTosan Ofogh developed a father strategy called Project Zagros, in which it was a combination of mean aversion and trend following which developed into machine learning.
Tosan Ofogh developed models that were technically based with fundamental features. Tosan Ofogh ’s strategy became technical but the data was fundamental. This model was also under two different subsections, Brent Crude Oil and S&P 500.
Present dayTosan Ofogh ’s current models are more cutting-edge versions of the previous models. Currently, R&D is in place for using S&P 500 channels for figuring out volatility in stock predictions. So, we have the Brent Crude Oil model, S&P 500 model and VIX Features. Through our development models, Tosan Ofogh has figured out that algorithmic and high-frequency trading can establish a more significant model revolution so that there could be more profit gained. It should also be noted that Tosan Ofogh is aware of the effects of news on stock market prices, which is why models are developed in a way that considers these parameters and Order Book Analysis.