#quant consulting

We can backtest and optimize your trading strategies against historical data.

We can also automate your trading strategies by writing C++/python code to interact directly with the exchange

Do you have an idea for a trading strategy, but want to prove that it will work through backtesting against historical data? Or do you have a successful trading strategy but want to optimize the parameters of the strategy to maximise returns?

Or perhaps you’ve heard about machine learning and would like to find out how you could incorporate it into your trading. Machine learning can be used to trawl through large amounts of data looking for statistically significant signals to use in your trading. It can also be used to determine the optimal way to combine a number of possible signals or ideas into a single algorithm.

We provide cloud-based PhD quant support for traders. We offer trading algorithm development services for equity and FX markets on all major exchanges. We also offer bitcoin and cryptocurrency algorithmic trading services on major exchanges like Binance and Bitmex.

In fact, we offer a wide range of quant (quantitative finance) consulting services including algo trading, derivative pricing and risk modelling. We also offer more general mathematical and algorithmic consulting services.

We create custom-built backtesting software in languages like C++ and python for individual traders and institutions to backtest and optimize their strategies.

This includes strategies on equities, FX, options, futures and cryptocurrencies like Bitcoin.

While many exchange/brokers have some built-in functionality (for example for backtesting), it’s going to be limited in scope. For one thing, when doing data analysis and machine learning to find profitable signals, you may wish to use a large amount of data external to the exchange. For another, in order to backtest a strategy you first have to implement it. For more sophisticated strategies, these high-level interfaces will not suffice and you’re going to need to write computer code. For example, technical analysis patterns are actually non-trivial to implement in code, particularly since the trader may rely somewhat on intuition and not know exactly how to make the signal precise.

Furthermore, any pre-existing commercial software for algo trading is likely to be very expensive, very complex to set up, and inflexible. In order to provide the necessary flexibility, it would have to allow you to incorporate your own code. But in this case, why not just get a custom application built in the first place?

And languages like python have an excellent selection of built-in mathematics and statistical toolkits which you can use both to design your strategy, and to backtest it.

So we recommend getting software custom built for your specific needs and application. All major exchanges these days allow you to use coding languages like python to grab data which can be used in your backtest, and automate the posting of buy/sell orders. We create bespoke algo trading software to allow you to

Backtest your strategy against historical data

Optimize the parameters of your strategy to maximise profitability

Automate your strategy to automatically post buy/sell orders to the exchange

Whether you’re a lone day trader looking to test your strategy, or a sizable organisation looking for get your feet wet with algorithmic trading and machine learning, our cloud-based quant consulting service has got you covered.

In fact, we offer a wide range of quant (quantitative finance) consulting services including algo trading, derivative pricing and risk modelling. We also offer more general mathematical and algorithmic consulting services.

Implied volatility surfaces are usually constructed from the market prices of a number of options of different strikes and expiries. Because of poor data quality, a trader valuing options from such a surface will typically generate arbitrage opportunities. In fact, even if the pillar observations are free of arbitrage, cubic spline interpolation can introduce arbitrage.

Volatility smoothing is the process of finding the cubic spline surface that fits the input data as closely as possible, under the restriction that it be arbitrage free. This turns out to be a quadratic optimization problem, as discussed in the very useful paper "Arbitrate Free Smoothing" of M.R Fengler.

We've implemented Fengler's algorithm in python, and in this short article we illustrate how it can be used to dramatically improve the quality of volatility data. The algorithm is fast enough to be run across millions of rows of volatility data.