This log indicates that your backtest script ran successfully and produced a report. Line‑by‑line interpretation:

1.

`[find_instrument_token] Persistent-cache-load-hui: 41 symbols (disk-se)`

→ The script loaded instrument tokens for 41 symbols from a local/persistent cache on disk. So your universe = 41 instruments.

2.

`SIA -- Downside-Fib-Signal -> Options-Writing (Call-sell) THEORETICAL-Backtest`

→ Strategy being tested:

- Signal: “Downside-Fib-Signal” (likely some Fibonacci-based downside event / retracement trigger).

- Action: Options writing on the call side (short calls).

- Nature: THEORETICAL backtest (no guarantee it matches live LTPs/IVs or real fills).

3.

`⚠️ HONEST: Historical-IV-data-uplabdh-nahi — Black-Scholes-synthetic-pricing (Realized-Vol x 1.2 VRP-bias), asli-market-prices-nahi.`

→ Important disclaimer:

- Historical implied vol (IV) data was not available.

- Option prices were synthetically generated using Black–Scholes with:

- Volatility = realized volatility × 1.2 (to approximate a volatility risk premium).

- These are model prices, not the actual historical option prices from the exchange.

Implication: P&L, drawdowns, returns etc. are indicative only and may be materially different from real tradeable history.

4.

`OK 1684 Downside-events mile.`

→ Total number of downside signal events detected: 1684 over the entire sample and symbol set.

5.

`OK Design(b): SKIPPED (genuinely-tested-infeasible, upar-comment-dekhein)`

→ “Design (b)” variant of the strategy was attempted earlier and found practically infeasible (likely in terms of execution, margin, liquidity, or data-availability), therefore it is skipped here.

6.

`PRIMARY(a, Fib-anchored): 1463 events, PRIMARY(c, ChatGPT-fixed-delta): 1684 events, skip: 0`

→ You are now evaluating two main strategy designs:

- PRIMARY (a): Fib-anchored variant

- 1463 valid tradeable events generated (likely a subset of all 1684 signals, possibly filtered by some Fibo anchor / structure).

- PRIMARY (c): ChatGPT-fixed-delta variant

- 1684 events (looks like it uses all detected downside events, but structures the option trade by targeting a fixed delta – for example, always selling ~0.20 or ~0.30 delta calls, as suggested by a ChatGPT design step).

- `skip: 0` → No events were dropped at the final stage for these primary configs (beyond earlier infeasibility filter for design b).

7.

`OK Report save hui: backtest_data\issue11_results\downside_fib_options_writing_report.md`

→ The full detailed backtest report is saved as a Markdown file at:

`backtest_data\issue11_results\downside_fib_options_writing_report.md`

You can open this in any text editor, VS Code, or Markdown viewer to see:

- Per-trade entries/exits (if logged)

- Aggregate metrics (CAGR, Sharpe, Max DD, win rate, average payoff, etc.)

- Breakdown by symbol / year / regime, depending on how you coded it.

Key practical points before relying on these results:

- Because IV and option prices are synthetic, slippage, bid–ask spread, and real IV skew/smile are not captured accurately. Real returns will likely be worse than theoretical.

- For index options (NIFTY/BANKNIFTY/FINNIFTY) or stock options, margin requirements and physical settlement constraints are not modeled here unless you coded them explicitly.

- Use this backtest as a hypothesis filter only. If performance looks promising, next steps:

- Re-run on a smaller universe but with actual option price data (e.g., from NSE bhavcopy/option chain vendors) if available.

- Stress-test with higher transaction costs and slippage.

- Check performance robustness across sub-periods and across symbols (ensure no single stock or year dominates P&L).

If you have any further queries, please connect with us on 022-6290-10141 (Timings : 09.00 AM to 05.00 PM) or you can email us on info@cniinfoxchange.com