How to Use the Backtester

Here is a step by step guide for how to log in and use the backtesting system.

By mltiplAI support

• 3 articles

Logging Into the Backtester

How to Log In to the Backtesting System Our backtesting system connects directly to Telegram using the official Telegram API. This allows us to securely access message history for analysis. Step 1: Connect Your Telegram Account On the login screen, enter your full phone number, including: - A leading + - Your country code - No spaces or special characters Example: +12345678910 Step 2: Enter the Telegram Verification Code Telegram will send you a one-time verification code inside the Telegram app. ⚠️ Important: - The code is not sent via SMS - It will appear as a message from Telegram in your app Enter the code and click Verify. If you do not receive a code, please see our 👉 Telegram Login Troubleshooting & Security article for more information about Telegram’s security protocols. Step 3: Select a Channel and Date Range Once logged in, you’ll see the backtesting dashboard. 1. Use the dropdown menu to select a Telegram channel 2. Choose a start date and end date 3. Click Extract Data to begin 4. Once you extract the first dataset, you'll see a dropdown with previously extracted datasets, as well as a preview when the dataset is loaded. ✅ Best Practice: We strongly recommend testing one month at a time. Why? - Telegram limits long-running data requests - Large date ranges can cause Telegram to disconnect your session - Messages are parsed serially, so bigger datasets take longer to process Shorter date ranges are faster, more reliable, and reduce errors.

Last updated on Jan 29, 2026

Editing Parsed Signals in the Backtester

How to Edit Parsed Trades in the Backtester Some Telegram signals contain typos or obvious pricing errors. To handle this, the backtester allows you to view and edit parsed trade data before re-running a test. Follow the steps below to access the dataset and make changes. Step 1: Open the Parsed Dataset Once a dataset has been extracted and loaded into the backtest module: 1. Locate the gray View Dataset button 2. Click View Dataset The dataset will open in a new browser tab, displaying all parsed trades in table form. Step 2: Locate the Trade You Want to Edit In the dataset view, each parsed trade is shown as a row. You can find a specific trade by: - Timestamp - Symbol - Entry price - Direction (buy/sell) - Stop loss or take profit values Use search, sorting, or scrolling to locate the trade you want to modify. Step 3: Edit the Stop Loss or Take Profit 1. Click directly on the SL or TP value in the table 2. The value will become editable 3. Enter the corrected price 4. Click anywhere outside the field to confirm the change At this stage, the value is updated locally but not yet saved. Step 4: Save Your Changes After making one or more edits: 1. Click the Save button 2. The dataset is updated with your corrected values ⚠️ If you leave the page without saving, your changes will be lost. Step 5: Re-Run the Backtest Once your edits are saved: 1. Return to the backtest module 2. Click Run Test 3. The backtester will use the updated dataset You do not need to reconnect to Telegram or re-extract messages. Important Notes - Only Stop Loss (SL) and Take Profit (TP) values are editable - Original raw Telegram data is preserved - Changes apply only to your current dataset - Editing trades will change backtest results This feature is intended to correct clear and obvious data errors, not to optimize performance after the fact.

Last updated on Jan 29, 2026

How to Use the “Compare Datasets” Module

How to Use the “Compare Datasets” Module The Compare Datasets module allows you to analyze two backtesting datasets to see: - How many trades overlap - Which trades match by entry and target prices - Who sent the trade first - The time delay between similar trades This makes it easy to identify copied signals and understand portfolio risk exposure. Why This Feature Is Useful Not all signal providers generate original trades. Some providers: - Copy trades from other channels - Forward trades with delays - Slightly reword messages to hide the source Thanks to modern data analysis, this behavior is easy to detect. By comparing datasets, you can: - See whether two providers are sending the same trades - Identify the original source vs. the follower - Measure how late copied trades arrive - Avoid stacking correlated risk in prop firm accounts A late trade is often less valid than the original due to price movement — and copied trades can silently distort your overall risk. Step 1: Open the Compare Datasets Module 1. Navigate to the Compare Datasets tab in the backtester 2. You’ll see two dropdown menus labeled for dataset selection Step 2: Load the Datasets 1. Select the first dataset from the left dropdown 2. Select the second dataset from the right dropdown 3. Click Compare Datasets The system will analyze both datasets automatically. Step 3: Review the Comparison Report Once the comparison is complete, you’ll see several sections: Matched Trades Summary This shows: - The number of matched trades - Matches are determined by entry price and target price Only trades that meet matching criteria are counted. Entry Price Scatterplot A scatterplot displays: - All matching entry prices - Visual clustering of overlapping trades Tight clusters often indicate direct copying or near-simultaneous signals. Instrument Overlap This section shows: - Which symbols/instruments appear in both datasets - How concentrated the overlap is High overlap across the same instruments can indicate shared signal sources. Time Delta Analysis This shows: - The time difference between matching trades - How long after the original trade the second one appeared You may see: - Trades grouped just minutes apart - Others delayed 20 minutes to 2+ hours Consistent delays strongly suggest copying behavior. How to Interpret the Results - Small time deltas → possible mirroring or automated forwarding - Large time deltas → manual copying or late signal delivery - High overlap + consistent delays → strong evidence of trade reuse This is especially important for prop firm risk planning, where duplicated exposure can violate rules or increase drawdown risk. Important Notes - Comparisons are read-only - No data is modified - Results are based on parsed trade data, not message text - Matching is price-based, not wording-based

Last updated on Jan 29, 2026