Daily Straddle Heatmap
AlgoTest heatmaps make it easier for you to understand a clear picture of your trading performance. These heatmaps visually represent data like time, performance metrics, and win rates. Think of them as a map highlighting where your strategies work best and where they stumble. For example, a heatmap might reveal that trades during certain hours of the day consistently yield higher profits.
We are taking four heatmaps today. Let’s understand what we can learn from them.
1. Banknifty Short Straddle:

- Observations:
- The strategy performs best at 10:45 (SL 40), yielding 2998 points.
- The worst time is 11:15 (SL 10), with a loss of -687 points.
- The performance stabilizes across SL values above 40, as profits remain consistent across those levels.
- Total Profit: 1356 points (SL 20-100 consistent).
- Insights:
- Using higher SL values (50 or more) seems optimal for consistent performance.
2. Finnifty Short Straddle:

- Observations:
- Best time: 09:45 (SL 30-100), yielding profits of 2934 points.
- The performance remains consistently positive until 12:15, after which results decline.
- Losses are observed during 13:15 to 14:45, with the lowest result being -344 points (SL 30) at 13:15.
- Total Profit: 88288 points.
- Insights:
- Early entries (before noon) generate the most profits.
3. Midcpnifty Short Straddle:

- Observations:
- Consistently underperforms across most SL levels and time slots.
- Best time: 10:15 (SL 10) with a profit of 1978 points.
- Worst period: 14:15, with small gains/losses showing poor opportunities overall.
- Total Loss: -128408 points.
4. Nifty Short Straddle:

- Observations:
- The total performance across all time slots and stop-loss (SL) levels is -153,529 points, indicating that the strategy performed poorly overall.
- Best Time: 10:15 (SL 10): Profit of 3750 points, the highest individual result.
- Worst Time: 09:45 (SL 100): A large loss of -6904 points.
- Total Loss: -153,529 points.
You can also check heatmaps for MidCapNifty, Nifty, BankNifty, and FinNifty across different dimensions to better understand market trends.