- Algorithmic Trading Software - AlgoTrader
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With more than 6 years of experience in trading in the stock market, he currently works as a quantitative analyst and strategist in an Argentine quantitative asset management firm and as a financial consultant for large corporations. Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available. Front Matter Pages i-xx. Introduction and Summary.
Algorithmic Trading Software - AlgoTrader
Pages Fixed Income. The second will be individuals who wish to try and set up their own "retail" algorithmic trading business. Quantitative trading is an extremely sophisticated area of quant 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. All quantitative trading processes begin with an initial period of research. You will need to factor in your own capital 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. 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 from this mean will eventually revert.
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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 which holds assets longer than a trading day. Correspondingly, high frequency trading HFT generally refers to a strategy which holds assets intraday.
Ultra-high frequency trading UHFT refers to strategies that hold assets on the order of seconds and milliseconds. As a retail practitioner HFT and UHFT are certainly possible, but only with detailed knowledge of the trading "technology stack" and order book dynamics. We won't discuss these aspects to any great extent in this introductory article. Once a strategy, or set of strategies, has been identified it now needs to be tested for profitability on historical data. 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". However, backtesting is NOT a guarantee of success, for various reasons.
Best Algorithmic Trading Books
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 bias , survivorship bias and optimisation bias also known as "data-snooping" bias. Other areas of 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. 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, 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. In order to carry out a backtest procedure it is necessary to use a software 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 equity curve over a particular time period usually annual.
Algorithmic Trading in Practice
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.
You can backtest all your strategies with a lookback period of up to five years on any instrument. Backtesting lets you look at your strategies on chronicled information to decide how well it would have worked within the past. Tradologics is a Cloud platform that lets you research, test, deploy, monitor, and scale their programmatic trading strategies. Stats are automatically generated for every backtest and detailed statistical reports available on demand. Allows you to go live and profit from your investment strategy where you will receive daily email updates about any live orders.
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