Friends somebody out of the blue complained and linkedin suspended my account without even giving me any notice. This is strange. LinkedIn has removed my article post and blocked my account. I have not received any email from linkedin about it whatsoever.

My latest post has been removed: https://www.linkedin.com/pulse/trading-venture-proposition-ahsan-amin/

When I tried to change my password, I was told that I needed to upload an identity verification document. I uploaded image of my passport and I later received an auto-generated message about the passport that my passport will be reviewed in 3-5 days.

Again I am looking to start a small Hedge Fund in HK/China though I can consider some place in mainland Europe if I can get a good deal. My email is anan2999(at)yahoo(dot)com.

You can call me on whatsapp but you have to send a message earlier. My mobile whatsapp is +92-336-2602125

Here is the content of post on linkedin at web address: https://www.linkedin.com/pulse/trading-venture-proposition-ahsan-amin/

Trading Venture Proposition.

Algorithmic Model Description.

Our model is close to high frequency trading models where trades take place roughly every thirty seconds to a minute.

Our model makes a prediction of the market fifteen seconds ahead and then makes decision to buy the stock asset if the market is predicted to go up and sell it if it is predicted by the model to go down. Once a trade has been made, we keep the trade alive after fifteen seconds if it is profitable to continue the trade and end the trade if it is not profitable to continue the trade.

Depending upon the market conditions, our model makes between six hundred to one thousand trades per day with average lifetime of a trade around thirty seconds to more than a minute.

We enter the trades through limit orders and earn a portion of bid-ask spread.

Our trading style and philosophy is similar to that of the most profitable firms on Wall Street especially Renaissance Technologies that makes billions of dollars in profits every year. We believe we have equally good trading algorithms.

Machine Learning Techniques Behind the Model.

Our model uses predictive machine learning to make trading decisions. We do research on independent factors that affect the short-term evolution of the financial asset and then apply machine learning to optimize for the parameters that best describe the short-term dynamics of the financial asset.

In our back-testing simulations, these optimized parameters are learnt from previous day’s data and are used to make trading decisions during the following day.

We tested our algorithms on large scale data with above rolling window of training on 24 hours of data and then applying the model to next 24 hours of data.

Our machine learning method is a new technique and is not known to people in financial markets. This results in more accurate prediction of the market than any time series model or process that analysts and quants usually use in financial markets to make a prediction of financial assets.

Calculation of Profits Associated with Our High Frequency Algorithmic Trading.

Author has experience with American stocks data and have previously run several algorithms on Nasdaq Stocks. However empirical calculations of profit and loss with this algorithm were done on major crypto currencies including Bitcoin and Ethereum using Binance data. Our algorithms consistently turned profits on both cryptocurrencies despite that they are far more volatile as compared to US stocks. Our algorithms consistently made profits in both highly bullish and highly bearish markets.

We used zero leverage in our projection of profits and costs.

In our trading back-testing, we made a profit of 1.5 cents per hundred dollars without including any earned spreads. Our models made roughly about one thousand trades per day as crypto market remains open for 24 hours. For high volume traders that trade Binance futures, Binance pays a rebate of one cent per hundred dollars. Adding our profits and rebate means we can make 2.5 cents per hundred dollars per trade on average. There are roughly one thousand trades per day taking our profits to (1000 *.025/100=25%) per day.

At this point, I will stop and reassure the reader that these profits are real and I can demonstrate them in independent back-testing. Such profits became possible due to our superior statistical machine learning model that correctly captures 20%-30% of all the variation in the financial assets.

When we applied our model on Cryptocurrencies, our Sharpe ratio was roughly 2.0. This is considered excellent Sharpe ratio with little downside risk.

In our back-testing simulations with Bitcoin and Ethereum there were several months in the data in which we made no loss during any single day of the month.

In the past, Author has applied vastly inferior models on Nasdaq stocks data and was able to find reasonable profits with those models. If this superior algorithm is run on Nasdaq stocks data, it can easily earn one cent per hundred dollars after accounting for round trip brokerage costs associated with every trade. This would result in extremely profitable trading strategy for Nasdaq stocks trading.

Again, very small but consistent profits when aggregated over a large number of trades become very significant and usually become several percentage points of the capital for even a single day.

Independent Verification of the Model

We fully welcome independent back-testing and verification of the claims made about profitability of our trading algorithms. Though we would not be willing to reveal machine learning techniques behind the algorithm, there can be several ways in which we can pass on parameters from my machine learning optimization that can then be used on future data to independently verify my claims about the profitability of the algorithms. In fact, I welcome friends to independently verify the model before sponsoring any capital for the new company.

Risks Associated with the Model.

There are much smaller risks associated with our fast and, short term trading style as compared to plain vanilla buy and hold strategies. When we make quick trades, it also becomes easier to limit losses since risk management techniques can be used when model loses money in small successive trades before the accrued losses could become large to make a significant decline in previously earned profits. Our trading style results in very high Sharpe Ratio for most of the financial assets we used in historical study. Our trading style is relatively safer as compared to existing trading strategies used to make profits in financial markets. While trading stocks, we do not intend to use any leverage which would make risk management of our models much easier. Before trading financial assets, we strongly vet that there are little known risks of large unexpected moves in the target stock. Our vetting stocks for large unexpected moves, our short-term trading style and zero leverage ensures that risk management of the trading portfolio remains relatively simple and straightforward process.

Prospective Financial Markets for Trading.

We want to use our model to trade HK stocks, Nasdaq Stocks, Stock exchange Index futures, currencies and commodities like oil and gold futures.

Limitations of the Model.

We have tried our models extensively on historical data but we have not done live trading with our models. We want to train our models comprehensively on paper trading with some good brokers and then use them for live trading in the final step.

Capital Requirements.

We will require initial amount of roughly two hundred thousand dollars. We need money for office space, a few fast computers, and high-speed internet. We will also need to hire about a staff of three to four mathematicians and programmers. We will also need infrastructure to secure the property.

As the profitability of our models becomes evident, we will continue to scale the trading capital with time once our market trading strategies continue to generate large profits. As we demonstrate that our trading strategies remain profitable with time, we will gradually increase the trading capital to several hundred million dollars.

About the Author.

Author is a mathematician who has made several important discoveries in mathematics.

For instance, millions of computers in tens of thousands of financial institutions use Monte Carlo simulations of stochastic differential equations every day to price derivatives and project risk of financial portfolios. Author discovered for the first time how to do higher order accurate Monte Carlo simulations of stochastic differential equations (SDEs).

Fokker-Planck partial differential equation is among ten major equations of mathematics and describes the time evolution of probability densities associated with SDEs. For the first time, author discovered several analytic ways to solve the Fokker-Planck equation.

Author has found analytic series solutions to first order ODEs, nth order ODEs and systems of ODEs. The series solution presents the true series of the analytic solution of the ODE. The method applies to all ODEs unless the ODE is singular when it has to be transformed into a non-singular form.