Quantitative trading is a strategy that uses mathematical functions to automate trading models. In this type of trading, backtested data are applied to various trading scenarios to spot. Data is provided as is without warranty of any kind. We do not generate, produce, nor resell the data. Instead, our service is to provide a convenient data format for . Forex Factory is for professional foreign-exchange traders. Its mission is to keep traders connected to the markets, and to each other, in ways that positively influence their trading results.
In this article I'm going to introduce you to some of the basic concepts which accompany an end-to-end quantitative trading system. This post will hopefully serve two audiences. The first will be individuals trying to obtain a job at a fund as a quantitative trader. The second will be individuals who wish to try and set up Forex quantitative trading own "retail" algorithmic trading business.
Quantitative trading is an extremely sophisticated area of Forex quantitative trading finance. It can take a significant amount of time to gain the necessary knowledge to pass an interview or construct your own trading strategies. However as the trading frequency of the strategy increases, the technological aspects become much more relevant, Forex quantitative trading.
All quantitative trading processes begin with an initial period of research. You will need to factor in your own Forex quantitative trading requirements if running the strategy as a "retail" trader and how any transaction costs will affect the strategy. Contrary to popular belief it is actually quite straightforward to find profitable strategies through various public sources. Academics regularly publish theoretical trading results albeit mostly gross of transaction costs.
Quantitative finance blogs will discuss strategies in detail. Trade journals will outline some of the strategies employed by funds. You might question why individuals and firms are keen to discuss their profitable strategies, especially when they know that others "crowding the trade" may stop the strategy from working in the long term.
The reason lies in the fact that they will not often discuss the exact parameters and tuning methods that they have carried out. These optimisations are the key to turning a relatively mediocre strategy into a highly profitable one, Forex quantitative trading. In fact, one of the best ways to create your own unique strategies is to find similar methods and then carry out your own optimisation procedure.
A mean-reverting strategy is one that attempts to exploit the fact that a long-term mean on a "price series" such as the spread between two correlated assets exists and that short term deviations Forex quantitative trading this mean will eventually revert, Forex quantitative trading.
A momentum strategy attempts to exploit both investor psychology and big fund structure by "hitching a ride" on a market trend, which can gather momentum in one direction, and follow the trend until it reverses. Another hugely important aspect of quantitative trading is the frequency of the trading strategy.
Low frequency trading LFT generally refers to any strategy Forex quantitative trading holds assets longer than a trading day. Correspondingly, high Forex quantitative trading trading HFT generally refers to a strategy which holds assets intraday, Forex quantitative trading.
Ultra-high frequency trading UHFT refers to strategies that hold assets on Forex quantitative trading order of seconds and milliseconds. As a retail practitioner HFT and UHFT are certainly possible, Forex quantitative trading, but only with detailed knowledge of the trading "technology stack" and order book dynamics. We won't Forex quantitative trading these aspects to any great extent in this introductory article, Forex quantitative trading.
Once a strategy, or set of strategies, has been identified it now needs to be tested for profitability on historical data, Forex quantitative trading. That is the domain of backtesting. The goal of backtesting is to provide evidence that the strategy identified via the above process is profitable when applied to both historical and out-of-sample data. This sets the expectation of how the strategy will perform in the "real world", Forex quantitative trading.
However, backtesting is NOT a guarantee of success, for various reasons. It is perhaps the most subtle area of quantitative trading since it entails numerous biases, which must be carefully considered and eliminated as much as possible. We will discuss the common types of bias including look-ahead biassurvivorship bias and optimisation bias also known as "data-snooping" bias.
Other areas Forex quantitative trading importance within backtesting include availability and cleanliness of historical data, factoring in realistic transaction costs and deciding upon a robust backtesting platform. We'll discuss transaction costs further in the Execution Systems section below, Forex quantitative trading.
Once a strategy has been identified, it is necessary to obtain the historical data through which to carry out testing and, perhaps, refinement. There are a significant number of data vendors across all asset classes. Their costs generally scale with the quality, Forex quantitative trading, depth and timeliness of the data. The traditional starting point for beginning quant traders at least at the retail level is to use the free data set from Yahoo Finance.
I won't dwell on providers too much here, rather I would like to concentrate on the general issues when dealing with historical data sets, Forex quantitative trading. In order to carry out a backtest procedure it is necessary to use a Forex quantitative trading platform. One of the benefits of doing so is that the backtest software and execution system can be tightly integrated, even with extremely advanced statistical strategies.
For HFT strategies in particular it is essential to use a custom implementation. When backtesting a system one must be able to quantify how well it is performing.
The "industry standard" metrics for quantitative strategies are the maximum drawdown and the Sharpe Ratio. The maximum drawdown characterises the largest peak-to-trough drop in the account Forex quantitative trading curve over a particular time period usually annual, Forex quantitative trading. This is most often quoted as a percentage. LFT strategies will tend to have larger drawdowns than HFT strategies, due to a number of statistical factors. A historical backtest will show the past maximum drawdown, which is a good guide for the future drawdown performance of the strategy.
The second measurement is the Sharpe Ratio, which is heuristically defined as the average of the excess returns divided by the standard deviation of those excess returns.
Note that annualised return is not a measure usually utilised, as it does not take into account the volatility of the strategy unlike the Sharpe Ratio. Once a strategy has been backtested and is deemed to be free of biases in as much as that is possible!
An execution system is the means by which the list of trades generated by the strategy are sent and executed by the broker. Despite the fact that the trade generation can be semi- or even fully-automated, the execution mechanism can be manual, semi-manual i. For LFT strategies, manual and semi-manual techniques are common. For HFT strategies it is necessary to create a fully automated execution mechanism, which will often be tightly coupled with the trade generator due to the interdependence of strategy and technology.
The key considerations when creating an execution system are the interface to the brokerageForex quantitative trading, minimisation of transaction costs including commission, slippage and the spread and divergence of performance of the live system from backtested performance. There are many ways to interface to a brokerage. They range from calling up your broker on the telephone right through to a fully-automated high-performance Application Programming Interface API.
Ideally you want to Forex quantitative trading the execution of your trades as much as possible. This frees you up to concentrate on further research, as well as allow you to run multiple strategies or even strategies of higher frequency in fact, HFT is essentially impossible without automated execution. As an anecdote, in the fund I used to be employed at, we had a 10 minute "trading loop" where we would download new market data every 10 minutes and then execute trades based on that information in the same time frame.
This was using an optimised Python script. In a Forex quantitative trading fund it is often not the domain of the quant trader to optimise execution. Bear that in mind if you wish to be employed by a fund. Your programming skills will be as important, if not more so, than your statistics and econometrics talents! Another major issue which falls under the banner of execution is that of transaction cost minimisation. Note that the spread is NOT constant and is dependent upon the current liquidity i.
Transaction costs can make the difference between an extremely profitable strategy with a good Sharpe ratio and an extremely unprofitable strategy with a terrible Sharpe ratio, Forex quantitative trading. It can be a challenge to correctly predict transaction costs from a backtest.
Entire teams of quants are dedicated to optimisation of execution in the larger funds, for these reasons. Consider the scenario where a fund needs to offload a substantial quantity Forex quantitative trading trades of which the reasons to do so are many and varied!
By "dumping" so many shares onto the market, they will rapidly depress the price and may not obtain optimal execution. Hence algorithms which "drip feed" orders onto the market exist, although then the fund runs the risk of slippage. Further to that, Forex quantitative trading, other strategies "prey" on these necessities and can exploit the inefficiencies.
This is the domain of fund structure arbitrage. The final major issue for execution systems concerns divergence of strategy performance from backtested performance. This can happen for a number of reasons. We've already discussed Forex quantitative trading bias and optimisation bias in depth, when considering backtests.
However, some strategies do not make it easy to test for these biases prior to deployment. This occurs in HFT most predominantly. There may be bugs in the execution system as well as the trading strategy itself that do not show up on a backtest but DO show up in live trading.
The market may have been subject to a regime change subsequent to the deployment of your strategy. New regulatory environments, changing Forex quantitative trading sentiment and macroeconomic phenomena can all lead to divergences in how the market behaves and thus the profitability of your strategy. The final piece to the quantitative trading puzzle is the process of risk management. It includes technology risk, such as servers co-located at the exchange suddenly developing a hard disk malfunction.
It includes brokerage risk, such as the broker becoming bankrupt not as crazy as Forex quantitative trading sounds, given the recent scare with MF Global! In short it covers nearly everything that could possibly interfere with the trading implementation, Forex quantitative trading, of which there are many sources.
Whole books are devoted to risk management for quantitative strategies so I wont't attempt to elucidate on all possible sources of risk here. Risk management also encompasses what is known as optimal capital allocationwhich is a branch of portfolio theory.
This is the means by which capital is allocated to a set of different strategies and to the trades within those strategies.
It is a complex area and relies on some non-trivial mathematics. The industry standard by which optimal capital allocation and leverage of the strategies are related is called the Kelly criterion.
Since this is an introductory article, I won't dwell on its calculation. The Kelly criterion makes some assumptions about the statistical nature of returns, which do not often hold true in financial markets, so traders are often conservative when it comes to the implementation.
Another key component of risk management is in dealing with one's own psychological profile, Forex quantitative trading. There are many cognitive biases that can creep in to trading. Although this is admittedly less problematic with algorithmic trading if the strategy is left alone!
A common bias is that of loss aversion where a losing position will not be closed out due to the pain of having to realise a loss. Similarly, profits can be taken too early because the fear of losing an already gained profit can be too great.
QuantForexTrading was developed by a team of Data Scientists and trading professionals with many years experience creating quantitative algorithmic trading strategies from Forex economic news data. All the strategies developed by the team have been backtested using the QuantForexTrading API and implemented for trading live using the Oanda trading API. Forex Factory is for professional foreign-exchange traders. Its mission is to keep traders connected to the markets, and to each other, in ways that positively influence their trading results. We offer the best in automated trading. Free yourself from emotions and become profitable trading any financial market (Futures, Stocks, Forex, ETF).