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

  • Highlights:
    • The 9:16 and 9:45 time slots show consistent profitability across all SL levels.
    • Losses appear more frequently after 11:15, particularly between 12:15 to 13:45.
    • The total profit is ₹9937, with SL levels between 10 and 20 showing the highest profits.
  • Observations:
    • Early entries (before 10:15) yield the most reliable profits.

2. Finnifty Short Straddle

  • Highlights:
    • Consistently profitable across most SL levels during the early hours (9:16 to 11:15).
    • Losses increase post 11:45, though they are smaller compared to Banknifty.
    • Total profit is ₹30986, with the 10 SL level yielding the highest returns.
  • Observations:
    • Trades entered after 13:15 show reduced profitability and higher variability.

3. Midcpnifty Short Straddle

  • Highlights:
    • Profitability is limited to earlier time slots (9:16 to 10:15), while the rest of the day shows consistent losses.
    • The total profit is ₹73, with significant losses for SL levels of 20 and above.
  • Observations:
    • Midcpnifty exhibits significant drawdowns after 10:45, making it unsuitable for intraday short straddles.

4. Nifty Short Straddle

  • Highlights:
    • Highest profits observed during the early trades (9:16 and 9:45).
    • Performance drops significantly after 10:45, with heavy losses in the afternoon.
    • Total profit is ₹28043, with SL levels of 10 and 40 performing best.
You can also check heatmaps for MidCapNifty, Nifty, BankNifty, and FinNifty across different dimensions to better understand market trends.