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good-high-vibe · 14 days ago

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To achieve a highly precise backtesting environment for GBP/USD with the flexibility to “zoom in” and “zoom out” across all timeframes (from 1-minute to weekly) and to view trades as they would have been executed in real time, we’ll need a robust setup that goes beyond typical backtesting tools. Here’s a breakdown of the most reliable tools, platforms, and data sources to achieve this level of precision.

Key Requirements for Precision Backtesting:

One, High-Resolution Data: Access to high-frequency tick data or minute-level data is essential. This data should cover extended historical periods, especially for lower timeframes like 1-minute charts.

Two, Data Accuracy and Granularity: Precise bid/ask data is necessary to simulate spread costs, slippage, and true price action at each timestamp.

Three, Multi-Timeframe Analysis: The backtesting environment should support multi-timeframe analysis so you can test strategies across 1-minute to weekly charts without compromising accuracy.

Four, Realistic Execution Simulation: The backtester must simulate real-world conditions, including spreads, slippage, latency, and order execution rules for all timeframes.

Recommended Setup for Precision Backtesting:

One, Data Source:

Dukascopy or TrueFX. Dukascopy offers high-quality tick data with bid and ask prices, often considered one of the best for FX backtesting. Data from Dukascopy is free and covers an extensive period for major forex pairs like GBP/USD. TrueFX also provides accurate historical tick data for forex pairs, which is ideal for precise backtesting. You can download data from these sources and clean it for high-precision backtesting, or use a provider like HistData for minute-level data if tick data isn’t necessary.

Two, Backtesting Framework: QuantConnect, Backtrader, or Zipline. QuantConnect is an advanced, cloud-based backtesting platform that can handle minute-level to tick data. It supports multiple asset classes and timeframes, allowing you to zoom in and out with precision. QuantConnect’s Lean engine can simulate realistic market conditions, including slippage, fees, and multi-timeframe analysis, in an institutional-grade environment. It’s free to use with paid options for cloud processing, but you can deploy it locally for free. Backtrader is a popular open-source framework that is highly customizable. While it doesn’t support tick data out of the box, it’s possible to extend it to handle tick data. Backtrader supports multi-timeframe analysis and is great for strategies that need 1-minute to weekly granularity. Zipline is another open-source option, but typically used more for equities. Zipline might need customization for forex trading and isn’t natively tick-compatible, which might make it less ideal for precise, tick-based forex backtesting.

Three, Precision Execution Simulation: AlgoSeek or Custom Slippage Models. For the most realistic simulation, using a tick-level backtester like QuantConnect allows you to implement custom slippage and execution models that mirror actual market dynamics. AlgoSeek offers accurate slippage modeling for backtesting environments if you need real-world order book data to enhance your simulation.

Putting It All Together in Python with QuantConnect’s Lean Engine (Tick and Multi-Timeframe Compatible):

Here’s an overview of how to achieve your goal with QuantConnect’s Lean engine for GBP/USD backtesting:

One, Acquire Historical Tick Data for GBP/USD using Dukascopy or TrueFX.

Two, Configure the QuantConnect Environment: Set up QuantConnect’s Lean engine locally and import the tick-level historical data. Define multiple timeframe indicators in your strategy code to switch between 1-minute and weekly views for analysis.

Three, Define Strategy and Execution Logic: QuantConnect allows defining multi-timeframe strategies easily. For example, you can set different indicators for 1-minute, 1-hour, and weekly timeframes and trigger trades based on conditions met across these timeframes.

Four, Implement Realistic Order Execution: Use QuantConnect’s slippage and spread settings, or write custom logic to mimic real market conditions for different timeframes.

Five, Run and Visualize the Backtest: You can visualize trades as they would occur on different timeframes and analyze the strategy’s behavior from the 1-minute level to the weekly level.

Additional Tips for Precision:

Tick-Based Slippage and Latency Models: Implement a custom slippage model if necessary, to account for market impact based on the volume and volatility. Spread Modeling: Forex spreads change based on market conditions, so make sure to use realistic, dynamic spreads rather than fixed ones. Order Book Data for Advanced Slippage Models: For ultra-precise modeling, consider using historical order book data if available (though it may come at a cost).

This setup provides a very high level of detail and realism, especially for forex pairs like GBP/USD, and will allow you to zoom in and out across different timeframes while maintaining realistic order execution and slippage settings.

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tradingsystemsforex · 7 years ago

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Automated Forex Trading Testing ... Testing 1, 2, 3 is a great article or trade setup posted on Winners Edge Trading

New Post has been published on http://winnersedgetrading.com/automated-forex-trading-testing-testing-1-2-3/

Automated Forex Trading Testing ... Testing 1, 2, 3

Mark Thomas — Trade On Track

An important step in the development of an automated forex trading strategy (trading robot or EA) is the testing phase. Testing should involve back-testing as well as forward-testing and be performed as accurately and extensively as possible. No amount of testing can absolutely assure future results, so the idea is to be as sure as possible!

Why Test?

The answer seems obvious … so that you can be reasonably sure of what you’ll get from your automated trading strategy in future. Testing means you can see how your strategy has reacted in the past to different market conditions. You can gauge whether the strategy has worked against different currency pairs or different time frames. You can test variations of the strategy to see what works best overall, and what should be avoided. Testing a strategy can give you the confidence to keep using the strategy in future if the past results have been profitable and consistent.

How do you test?

To back-test a trading strategy, you first need to obtain some historical data. The historical data contains information about price levels in the past, such as open, high, low and close information. You can get historical data for daily intervals (this is the easiest to get), where each record contains open, high, low, and close prices for each trading day. More detailed data can also be obtained for shorter intervals such as hourly, 5 minute or even 1-minute intervals. Generally, the shorter the time frame, the more accurate you can make your back-testing.

The first place to look for historical data is your broker. Many brokers offer free data downloads if you have a live account open with them, and the data is usually of quite good quality. Your broker’s historical data will also most likely be the same data that is used to draw the charts that you trade off, so it’s easy to look back through your charts and visually check how your strategy plays out.

There are other sources of data too, some are free, some involve a cost. It’s a good idea to obtain good quality data from a number of different sources for your back-testing. Data can vary, so it’s good to be able to test your strategy on the different data sources to see if it holds up. Dukascopy (http://www.dukascopy.com) is a good source of data, and you might also try Gain Capital (http://ratedata.gaincapital.com/)

To actually perform the back-testing, you’ll need a software program which processes the historical data and executes your trading strategy using that data. Trades aren’t actually executed, of course, the execution is just emulated within the software. Based on your strategy, the program can keep track of when the trades would have been taken, how profitable they were, how many winners compared to losers you had, and many other statistics related to the performance of your strategy.

The most accessible automated trading forex software program to do this is Metatrader 4 (MT4). It has a built-in strategy tester and you don’t even need to download any historical data first – it will do it for you automatically. The downside to MT4 is that you first need to write an EA which implements your trading strategy. Writing an EA involves writing a computer program in the MQL4 language – the scripting language that MT4 understands. So, you’ll need to be able to program in MQL4 or get a programmer to do it for you if you want to go down this route.

If you like to get your hands dirty in computer code, you can also just write a program to back-test the strategy using the historical data. There is usually a library available for your favorite language which contains the indicators and other statistics you may need to test the strategy, such as EMAs, SMAs, MACDs, RSIs and so on. For instance, there is a freely available library called “profitpy” for the Python programming language which allows you to build a strategy tester.

For non-programmers, check out http://www.thinkingstuff.com. Their system allows you to develop and back-test strategies using point-and-click technology. What this means is that you don’t have to write any programming code, you just use your mouse to select options and the Thinking Stuff software builds the back-testing code for you.

Other related sites for strategy testing include: http://forextester.com/ and http://forexsb.com/

Testing Pitfalls

Back-testing and getting good results doesn’t necessarily mean that you have a good automated trading strategy. There could be flaws in your back-testing procedure or data which is affecting your results. Some common back-testing problems include:

Optimizing (or curve-fitting) the strategy too much. If you tweak and re-tweak your trading strategy to give the best results possible using the data that you have available, then you run the risk of tailoring it too close to that data. If there are too many parameters or filters involved to make the strategy work, then you may have problems in getting that strategy to give satisfactory results in the future. Sometimes you’re better off NOT optimizing so much – give the strategy a little room to breathe, make it a little simpler or more flexible. A lightly curve-fitted strategy is likely to give better results in the future.

The data isn’t detailed enough. If you have 15-minute historical data and you’re testing a strategy that works on the 15-minute chart, then the accuracy of back-testing is going to be quite limited. For instance, if in one 15 minute period (one 15 minute bar) price shoots up and reaches a point that would have hit your target and it also reaches down to a place where it would have taken out your stop loss, how do you determine whether your target was hit first or your stop was hit first? The answer is, you can’t. Without going down to a lower time frame or even tick data, there is no way from your historical data that you can tell whether price went up first or down first. You only have open, high, low and close information. So, if your data is limited in detail, your back-testing program should take the worst case scenario in this example and assume that the stop was hit first.

Data is bad quality. Some free data sources are terrible quality. There are gaps in the pricing (from the close of one bar to the opening of the next bar), there are incorrect prices, and sometimes there is just no data where there should be some! Try to obtain decent quality data and when in doubt, try to obtain data from a number of sources and get the best results possible from all sources.

Forward-Testing

All of the above has dealt with back-testing, but forward-testing is also important. Forward-testing an automated trading strategy involves running the robot trading software in a live (or demo) trading account and monitoring the results over a period of time. You may be surprised that something that works really well when back-tested, does not perform very well when running on live data. This can be because of the curve-fitting problem mentioned above, or because you’re striking market conditions that haven’t been tested in the past. Forward-testing can also uncover other problems in your back-testing that you weren’t aware of, so it’s essential to forward test, preferably on a demo account before moving to a live account.

Once you’ve back-tested your strategy on a good sample of historical data and you’ve forward-tested it too, you can be confident that your future (or at least, near future) results will be fairly similar to the results you’ve had to date. Never just let a trading robot loose though – always monitor what it’s doing because markets can be unpredictable you want to be ready to pull the plug should something go wrong.

#Trading Robots #trading strategy

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