Fetching Strategies
The primary role of the AI Backtesting Assistant is to help users find strategies quickly without having to iterate through various conditions, tickers, and timeframes on TradingView charts. Instead, users can simply ask the AI to look for the strategy that would best match the criteria they want.
The AI Backtesting Assistant has access to thousands of unique strategies, half using our S&O toolkit and the other half using our PAC toolkit.
This AI tool’s backtesting data & analytics are for informational purposes only and do not constitute financial advice or recommendations to buy or sell any financial product, including but not limited to securities, derivatives, cryptocurrencies, or other investment instruments.
This tool may contain errors, and past performance is not indicative of future results. Always conduct your own research and consult with a professional financial advisor.
LuxAlgo is not liable for any decisions made based on any information given. Read our full disclaimer.
Strategies Configuration
Strategies accessible by the AI Backtesting Assistant are backtested using an initial capital of 10,000$, without commissions/slippage, and using default settings (except for the PAC which uses a different invalidation behavior).
Unit position sizing is used across all tickers, except forex pairs which use a standard lot (100,000 units).
The strategies the AI can access don’t use every single one of the features within the toolkits, see the section below to learn more about the supported tickers, timeframes, and backtesting conditions.
Any strategy with 0 closed trades will not be accessible by the AI.
Strategies do note make use of stop losses or take profits.
Supported Tickers
The following tickers are supported:
Supported Timeframes
5m, 15m, and 60m timeframes are supported.
S&O Conditions
AI strategies constructed from our Signals & Overlays (S&O) toolkit use a single condition or a combination of two conditions for the long and short entry conditions.
Only the following conditions are considered:
It is possible for a long/short entry condition to contain both bullish and bearish conditions
S&O Exits
Strategies using the S&O toolkit can make exit trades which are outlined below:
PAC Conditions
Strategies based on our Price Action Concepts toolkit (PAC) can use a maximum of three steps and invalidate entry conditions on any repeated step.
PAC strategies do not make use of any exit conditions.
How To Fetch Strategies
Interacting with the AI Backtesting Assistant is straightforward. All users need to do is type a prompt in the chat box located at the bottom of their screen.
Here are examples of prompts that will trigger the AI Backtesting Assistant to retrieve a trading strategy:
- What is the best performing strategy across all crypto tickers on the 5-minute timeframe?
- Give me three strategies using fair value gaps that have a winrate above 80%.
- Find a strategy with above average net profit but below average drawdown.
Users can also click on one of the suggested prompts in the search group located above the input prompt.
The AI can access the following backtesting results for strategies:
- Net Profit
- Executed Trades
- Percentage Winrate
- Max Drawdown Percent
- Profit Factor
- Average Trade
- Average Winning Trade
- Average Loosing Trade
These results will be returned in a table display within the chat. If multiple strategies are returned, they will all be located within that table. The AI can occasionally fetch strategies in the middle of a response; this will return another table containing the strategies fetched.
A maximum of five strategies can be returned in a table at once, while a maximum of three strategies can be returned from a single fetching operation.
If the AI needs to fetch more than 3 strategies it will fetch strategies in parallel.
The AI has also access to the following information from a strategy:
- Strategy start time: When the strategy started being evaluated
- Strategy current position (long, short, or none)
- Best signal sensitivity for a strategy using confirmation or contrarian signals
Fetch Statistics Across The Entire Strategy Population
The AI can also get general statistics and data across the whole population of strategies being backtested.
Example of prompts doing this are:
- What is the total net profit of all bitcoin strategies you have?
- On BTCUSD, are strategies using normal Confirmation signals better than the ones using strong Confirmation signals overall?
- What is total net profit of profitable strategies across only stocks?
This can be useful to get big picture information about strategies.
Add a Strategy to Your Chart
Once you get a strategy you like from our AI, you may be inclined to add it to your chart to visualize it & further optimize its results on TradingView.
To do so, follow these steps:
Add The Backtester To The Chart
Go to your TradingView chart, then open the indicator menu, locate the Backtester of the Toolkit related to the strategy you want to add. For example, if you found a strategy with our AI Backtesting Assistant that used Signals & Overlays, you would add the “Backtester (S&O)” strategy script to your TradingView chart.
Adjust Backtesting Starting Time
Go in the Backtester’s settings, then select “Date” in the “Backtest Window” setting, then adjust “Window Start” with the strategy start time provided by AI.
Select The Conditions
Adjust the long and short entry conditions in the backtester based on the conditions returned by the AI.
Strategies the AI has access to have a defined starting point. Users might not be able to reproduce exact strategy results due to the limitation on how much data certain users can access.
Saving Strategies
Users can export strategies displayed within a table as a csv using the download icon located under it.
Prompting Tips
The AI can make mistakes when fetching strategies, as this is a very complex process depending on the user query.
We expect to improve the AI Backtesting Assistant over time; however, the following tips can help you get the most from our AI.
Make Detailed Queries
Make sure to detail every aspect of the strategy you want to fetch if you have a precise one in mind, for example:
| Original | Improved |
|---|---|
| Fetch a strategy that uses Confirmation Signals | Fetch a strategy that only uses Confirmation Signals and no other conditions |
| I want a PAC strategy that goes long on a new FVG, and CHoCH | I want a PAC strategy that goes long when: - Step 1: a new bullish FVG occur - Step 2: a bullish CHoCH occur |
| I want the best strategy | I want the strategy with the highest net profit on BTCUSDT 5 minute |
Context and details will help the AI avoid making guesses about your query.
Encourage the AI to Improve
The AI Backtesting Assistant can be fairly self-aware if a fetched strategy doesn’t meet a user criteria, and will then retry.
But if the AI response is clearly mistaken, then it can be good to let it know using a prompt encouraging re-evaluation of the fetching process:
The strategy your returned is not the one I want. Correct the issue in your fetching process and retry to fetch the strategy in accordance with my query.
Split Complex Queries Into Multiple Tasks
The AI will be more prone to mistakes if the query is complex, as such, it is best to do follow-up messages asking about the details of a strategy rather than asking the AI to give those alongside other information.
For example:
Return a good strategy on BTCUSDT, alongside the average net profit of all strategies on BTCUSDT that use Confirmation signals, then tell me what the highest winrate strategy is, using the same conditions as the first one
The AI Backtesting Assistant might provide more relevant answers if we let context accumulate by splitting and re-ordering our initial query into multiple ones:
First Query
Return a good strategy on BTCUSDT
Second Query
Find the strategy with the highest winrate that use the same exact entry conditions returned previously
Third Query
Return the average net profit of all strategies on BTCUSDT that use Confirmation Signals.
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