At PyratzLabs, we are currently working on a decentralized options market maker: the next generation of Robinhood for crypto. In order to outperform our competitor, we have introduced to our trading strategy a deep learning algorithm to help reduce the risk. This deep learning algorithm, called Deep Hedging has been only developed by companies like JPMorgan or Mazars.
First, let me explain you the concept of Decentralised Options Market Maker. An option trading is how investors can speculate on the future direction of the overall market.
For example (considering BTC at 20k today): if I think the BTC market is going over 25k for the next 30 day, I’ll buy a call option with a maturity of 30 days at a strike price of 25k. At the end of this period, I have the right to execute my option, meaning that if the BTC is going over 25k, I’ll earn every $ over this amount. If is not achieve 25k, I’ll loose my premium.
Now that we understood option, let’s understand what is an automated options market maker (AOMM). An AOMM allows traders to buy and sell options on Bitcoin against a pool of liquidity (our protocol).
Since the protocol acts as a market maker, it aims to find an options trading strategy that aims to reduce (or hedge) the risk associated with price movements in the underlying asset : a Delta Hedging strategy. Put another way, we’ll hedge one investment by making a trade in another.
In traditional markets, the most popular models for valuing options called Black-Scholes. Unfortunately, this is not effectively applied to the crypto market microstructure as it is only optimal in a perfect market condition (Market efficiency and zero transaction cost assumptions, low Risk-free rate and volatility of the underlying…).
We seek to overcome this problem by adding a neural network to the delta hedging algorithm: we trained a neural network to trade the optimal hedge point in every market condition using Black Scholes as a label. The network we proposed receives as input the market information and the current delta position.
We’ll explain simply what is a neural network, how we used it on how it is interested to use it in our case.
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems. In our case, the problem we want to solve is to find a delta hedging strategy in a crypto market or in other word to adapt the traditional Black&Scholes trading strategy to a super volatile market.
Without going into details, we use deep learning techniques by training our data on a traditional market, once our algorithm is trained in this traditional environment we change its environment to a crypto environment.
We train our algorithm with four different deep learning algorithm (TCN, RNN, FNN,AttentionNet) and compare it with the traditional Black-Scholes method. Each models have these advantages and disadvantages, we compare them all and then we choose the one we will integrate in the protocol and here are the result:
First approximation:
We’re going to analyse every metric to conclude. As a metric we’ll look at:
For a second conclusion, we analysed the delta of B&S compared to the delta of our deep learning model. We took the delta hedging for our “model” and compare it to a classical delta hedge.
Remember: we train an algorithm to find at each time the best hedge point in order to reduce or risk. We aim to do better than B&S in a crypto environment.
In this paper, we leverage a set of state-of-the-art deep learning technologies to explore the landscape of neural delta hedging.
We seek to explain it to begineer with little Blockchain & Data background
We build a protocol who have the ability to minimize profit and loss for options trading introducing deep learning method.