Yes. I've recorded **all of today's findings as the official fix backlog for the Shadow Mode review.** I'll treat **13 Aug 2026 as the reference validation session.** ### Issue #1 (Critical): The strategy missed the biggest move of the day **Observed signal** | Time | Signal | Spot | Contract | Entry | Stop | Target | | ----- | ------ | -------: | ------------------- | ----: | ----: | -----: | | 11:25 | BUY_CE | 24327.25 | NIFTY18AUG2624400CE | 95.25 | 93.34 | 97.16 | **Actual market movement** * NIFTY moved from **24327 → 24413** * Total move: **+86 points** * The option premium expanded far beyond the defined target. **What we need to fix** * Target calculation is too conservative. * The strategy is behaving like a **scalping system**, while the market offered a **trend-expansion move**. * We need to determine whether the current target model should be: * Fixed risk/reward * ATR-based * Volatility-adjusted * Open-ended with a trailing exit --- ### Issue #2 (Critical): The trailing-stop mechanism did not capture the move You specifically reminded me that **a trailing-stop system already exists.** We must audit it. **Questions to answer** * Did the trailing stop activate? * Was it disabled? * Did it move to breakeven? * How often was it updated? * Was it trailing the 9 EMA? * Was it trailing the 20 EMA? * Was it percentage-based? * Did the position exit before the trailing logic became active? **Required evidence** For every trade, we need the complete lifecycle: ```text Signal generated ↓ Entry executed ↓ Stop initialized ↓ Trailing activated ↓ Stop adjustments ↓ Exit ↓ Exit reason ``` --- ### Issue #3 (Critical): No BUY_PE signal after the bearish reversal This is probably the most concerning issue. **Observed behavior** ```text 12:05 → Market peak 12:05–12:40 → Reversal begins 12:40–13:00 → Bearish continuation No BUY_PE signal generated ``` **Possible causes** * Downward EMA crossover was never detected. * Quality filter rejected it. * Confidence filter rejected it. * Cooldown prevented a second signal. * Only one active trade was allowed. * The CE position was still considered open. * Contract selection failed. * A signal-suppression rule blocked it. --- ### Issue #4 (High): The scoring model underestimated momentum The bullish breakout was labeled: ```text Strategy: ema_crossover Strength: 0.39 MarketTrend: Neutral TrendConfidence: 0 Quality: 33 ``` Yet the market immediately produced an **86-point directional move.** We need to reverse-engineer: ```text Strength formula TrendConfidence formula Quality formula Indicator weights Suppression weights Filter thresholds ``` We need to understand **why a major expansion move received such a weak score.** --- ### Issue #5 (High): Candle-by-candle replay of the entire session We'll reconstruct the complete session timeline: ```text 09:15 → Open 11:25 → BUY_CE generated 11:25–12:05 → Bullish expansion 12:05 → Session high 12:05–12:40 → Reversal 12:40–13:00 → Bearish continuation 15:30 → Market close ``` For **every signal**, we'll calculate: * Entry * Exit * MFE (Maximum Favorable Excursion) * MAE (Maximum Adverse Excursion) * Target hit * Stop hit * Trailing-stop activation * Trailing-stop updates * Exit reason --- ### Issue #6 (Medium): Warning messages appearing multiple times You also asked: > Why do warnings appear several times? We'll investigate: * Duplicate event emission * Repeated validation loops * Polling duplication * The same warning being written by multiple pipeline stages * Logging handlers registering more than once We'll verify whether the warnings are: ```text Signal warnings Quality warnings Capture warnings Shadow warnings Outcome warnings Logger duplication ``` --- ### After today's session closes, send me these outputs: ```bash python -m shadow_mode summary \ --limit 1000 ``` ```bash python -m shadow_mode outcomes \ --limit 1000 ``` ```bash python -m shadow_mode attach-outcomes \ --limit 1000 \ --dry-run \ --verbose ``` Also send: * Signals JSON * Observations JSON * Outcomes JSON * PAPER outcomes JSON --- ### One very important rule: **Do not modify any code before we analyze today's evidence.** Today's market session is now our **official replay dataset.** The workflow will be: ```text Collect today's data ↓ Identify the root cause ↓ Fix the algorithm ↓ Replay 13 Aug 2026 ↓ Compare old vs new results ↓ Verify bullish-move capture ↓ Verify bearish-reversal detection ↓ Validate trailing-stop behavior ↓ Approve changes ``` I've saved **all of today's observations** as the baseline for the next review. 13 Aug 2026: Yes, I **received the files. You can stop uploading now.** I can see all three datasets are now available in this conversation: 1. `option_prices.json` ✅ 2. `shadow_signals.json` ✅ 3. `nifty_1m_candles.json` ✅ I can now start the full post-market forensic review. My review will focus on the exact issues we identified during today's live session: 1. **The biggest intraday move was missed.** * Why wasn't the move detected? * Was the crossover generated too late? * Was it rejected by a filter? 2. **Trend reversal while the engine still generated `BUY_CE`.** * Why did the direction disagree with the chart? 3. **Missing `BUY_PE` after the market reversed downward.** * Why wasn't the PE signal generated? 4. **Targets are far too conservative.** * Example: ``` Entry: 95.25 Stop: 93.34 Target: 97.16 ``` * Why did the engine aim for only ~2 points while the contract later moved much further? 5. **Trailing stop behavior.** * Could the existing trailing-stop system have captured today's large move if the initial signal had been generated correctly? 6. **Repeated warnings.** * Why were identical warnings printed multiple times? 7. **Filter analysis.** * `regime_choppy_veto` * `m4_session_exhaustion` * `signal_quality` * `cooldown` * `trading_window` 8. **Signal timeline reconstruction.** * Rebuild the entire trading session minute by minute. * Compare: * NIFTY candles * Option prices * Generated signals * Rejected signals * Missed signals 9. **Historical replay.** * Re-run today's data offline. * Design a fix. * Verify the fix against today's session before changing production logic. After the review, I'll give you **one final implementation prompt** that your coding agent can execute to fix today's missed-move problem and retest it against the historical data. Brother, I've finished the **first full pass** through all three datasets. This is a **source-based review**. I'm only reporting what the files actually support. --- # Executive summary **Your intuition was correct.** The system **did not capture the strongest move of the day.** And the problem is **not the trailing-stop system.** The problem is upstream, in **signal generation**. The data points to **three weaknesses**: 1. **The EMA crossover reacts too late.** 2. **Profit targets are far too conservative.** 3. **The strategy repeatedly flips between CE and PE during transitional phases instead of recognizing trend continuation.** --- # Dataset review ## NIFTY candles The file contains: * **362 one-minute candles** Session: * **09:15 → 15:16 IST** --- ## Option prices The file contains: * **12,297 option candles** This gave me the complete intraday price history for every option contract. --- ## Shadow review The exported review contains: * **269 persisted decisions** --- # Actual signals found in the exported evidence | Time | Direction | Contract | Entry | Target | Quality | | ----- | --------- | -------- | -----: | -----: | ------: | | 10:44 | BUY_CE | 24400 CE | 96.25 | 98.18 | 35 | | 11:02 | BUY_PE | 24300 PE | 73.20 | 74.66 | 50 | | 11:25 | BUY_CE | 24400 CE | 95.25 | 97.16 | 33 | | 12:38 | BUY_PE | 24400 PE | 83.60 | 85.27 | 61 | | 12:41 | BUY_CE | 24400 CE | 132.35 | 135.00 | 30 | | 14:23 | BUY_CE | 24400 CE | 123.95 | 126.43 | 52 | | 14:42 | BUY_CE | 24400 CE | 130.20 | 132.80 | 31 | --- # Issue #1: the target is dramatically too small Example: ## 11:25 signal ``` BUY_CE Entry: 95.25 Target: 97.16 ``` Expected move: ``` +1.91 points ``` Risk: ``` Stop = 93.34 Risk = 1.91 points Reward = 1.91 points RR = 1:1 ``` --- Your own chart screenshot showed that the contract moved **much farther than 2 points**. The strategy is effectively saying: > "Take profit immediately after the crossover." That works in range-bound markets. It completely fails during **trend-expansion days**. --- # Issue #2: the strategy is oscillating Look at this sequence: ``` 11:02 BUY_PE 11:25 BUY_CE 12:38 BUY_PE 12:41 BUY_CE ``` Three minutes: ``` 12:38 BUY_PE 12:41 BUY_CE ``` That's a warning sign. A healthy trend-following system should not reverse direction in only three minutes unless there's an actual market reversal. --- # Issue #3: signal quality is not selecting the best moves Signal quality scores: | Time | Score | | ----- | ----: | | 10:44 | 35 | | 11:02 | 50 | | 11:25 | 33 | | 12:38 | 61 | | 12:41 | 30 | | 14:23 | 52 | | 14:42 | 31 | --- Your best signal scored **61**. Several signals were accepted with scores: ``` 30 31 33 35 ``` That tells me the scoring system isn't separating: * weak crossovers * trend reversals * explosive breakouts --- # Issue #4: the system isn't recognizing trend continuation This is probably the biggest problem. Your current logic is essentially: ``` EMA crossover ↓ Generate signal ↓ Calculate target ↓ Finish ``` But today's market required: ``` EMA crossover ↓ Breakout confirmation ↓ Trend continuation ↓ Trailing stop ↓ Trend exhaustion ↓ Exit ``` The second half of the model doesn't exist yet. --- # Issue #5: the trailing stop is not the problem You told me: > We already have a trailing stop. Good. Keep it. I would **not** modify the trailing stop first. I would modify **entry qualification** and **profit targeting**. --- # I found something else Your console summary reported: ```json signals: 19 rejected: 10 ``` But the exported evidence contains only: ```json Persisted SIGNAL events: 7 ``` That discrepancy should be investigated. I would verify: * Which signals were observed but never persisted. * Which signals were deduplicated. * Which signals were rejected. * Which signals disappeared between observation and storage. --- # My recommended fixes (in order) ### Phase 1 (highest priority) Replace: ```text EMA crossover ``` With: ```text EMA crossover + EMA separation + Breakout confirmation ``` --- ### Phase 2 Replace: ```text Fixed target ``` With: ```text ATR-based dynamic target ``` --- ### Phase 3 Add: ```text Trend continuation mode ``` Rules: ``` If a trend is active: Don't wait for another crossover. Stay with the trend. Trail the stop. Exit only after: - EMA recross - Trailing-stop hit - Trend exhaustion ``` --- ### Phase 4 Build a replay validator for **2026-08-13**. Replay every minute. Produce a report: ```text 09:15–15:30 For every minute: NIFTY ↓ EMA state ↓ Signal ↓ Selected option ↓ Entry ↓ Target ↓ Maximum favorable excursion ↓ Maximum adverse excursion ↓ Trailing-stop path ↓ Exit ``` --- **My recommendation: do not start modifying code tonight.** Tomorrow, let's do this properly: 1. Replay **2026-08-13**. 2. Find **every missed move**. 3. Identify the exact line of code that suppressed those trades. 4. Generate **one implementation prompt**. 5. Retest against today's historical session. I think that's the right next step. 14 Aug 2026: 11:25 BUY_CE 11:26 Entry 95.25 Stop 93.345 11:29 Premium 97.45 Stop 95.545 11:30 Premium 98.90 Stop 96.995 11:31 Premium 103.05 Stop 101.145 11:33 Premium 103.20 Stop 101.295 11:36 Premium 107.35 Stop 105.445 11:39 Premium 112.95 Stop 111.045 11:40 Premium 108.45 Exit What is the exit price here? 111.045 or 108.45? Because of as per the SL, it should be exit at 111.045. I want all the Shadow commands to be run from the Super Admin UI. There must be an option also to choose Index, like Nifty/BankNifty and for reply-batch it must be ask to choose from_date and to_date(Date should be dropdown and it must be from signals generated table) I mean, every time I have to go to the CLI and run the command. I want it on the Super Admin UI. There must be an example of each command also, like how to run. And I must press a button to run the specific command. I mean, there must be separate buttons for all the available Commands which is exists in the CLI. Give me prompt for this. My recommendation for the next phase: Don't tune anything. Don't change the 60 threshold. Don't enable the gate. Instead, focus on automating evidence collection. Your next milestone should be: Daily workflow: Market closes ↓ Shadow session finishes ↓ attach-outcomes ↓ replay-signals ↓ replay-batch ↓ quality-monitor ↓ quality_history upsert ↓ Generate daily evidence report Then let it run automatically for at least 20–30 trading sessions before touching the threshold again. At this point, I would say the signal-quality system has successfully moved from a filter into an evidence-gathering system, which is exactly where it should be right now. 15 Aug 2026 My genuine answer: **No, I would not move the 9 EMA strategy to live trading yet. But I would absolutely move it into a live evidence collection phase.** Those are two very different decisions. You already found one major defect: * Signals were correct. * Entries were correct. * The trailing stop itself was correct. * **The execution model was wrong.** * Profit changed from **+11.70 → +22.73** after the fix. That is a very significant change. The good news is that you found the bug **before production**. However, replay still has limitations: * Replays use stored 1-minute candles. * Real markets are tick-driven. * Replays cannot reproduce live quote updates exactly. * Network latency isn't included. * Broker latency isn't included. * Option chain updates aren't identical to live conditions. * Data synchronization issues can only be detected during a live session. I think your proposed workflow is the correct one: **Phase 1 (next week): Live shadow mode** ```bash python -m shadow_mode watch \ --symbol NIFTY \ --instrument index_option \ --interval 60 ``` ⬇ **Phase 2: Capture everything** * Every market observation * Every generated signal * Every rejected signal * Every accepted signal * Every entry * Every stop movement * Every trailing stop promotion * Every target update * Every exit * Every outcome ⬇ **Phase 3: Validate after market close** Run: ```bash python -m shadow_mode session-report \ --date YYYY-MM-DD \ --symbol NIFTY ``` ```bash python -m shadow_mode replay-signals \ --date YYYY-MM-DD \ --symbol NIFTY ``` ```bash python -m shadow_mode replay-batch \ --start YYYY-MM-DD \ --end YYYY-MM-DD \ --symbol NIFTY ``` ⬇ **Phase 4: Compare live evidence with replay evidence** Verify: * Did every signal appear in `ops_decisions`? * Did every entry occur at the correct timestamp? * Did every contract selection match? * Did every trailing stop promotion occur correctly? * Did the stop move when expected? * Did the stop execute at the correct price? * Did the target behave correctly? * Did the exit reason match the evidence? * Did `ops_decisions`, `session-report`, `replay-signals`, `replay-batch`, and the Super Admin UI all show the same numbers? ⬇ **Phase 5: Collect a statistically meaningful sample** I would not use **one day**. I would not use **two days**. I would collect at least: * 5 trading sessions (minimum) * 50+ trades (better) * 100+ trades (ideal) And I would track these KPIs: | Metric | Acceptance criterion | | ---------------- | -------------------- | | Signal count | 100% consistent | | Entry price | 100% consistent | | Stop execution | 100% consistent | | Trailing stop | 100% consistent | | Missing outcomes | 0 | | Missing trades | 0 | | Win rate | Stable | | Profit factor | Stable | | Data gaps | 0 | Only then would I move to the next phase. **My recommendation:** ❌ Not ready for real-money trading. ✅ Ready for live shadow validation. After 1 week of clean live evidence and 50+ validated trades, I would be comfortable saying: > The 9 EMA strategy is production-ready. That's my honest answer. I would not skip the live evidence phase. 16 Aug 2026: 08:45–09:15 ↓ Shadow Command Center 09:15–15:30 ↓ Live monitoring 15:30–16:00 ↓ Post-market workflow After 16:00 ↓ Research and replay Night ↓ Review ops_decisions ↓ Compare 11 Aug ↓ Compare 13 Aug ↓ Compare 17 Aug 17 Aug 2026: Investigate why event sev_d3571bc0241d6472b78eae83c34f8d86 generated a BUY_CE signal at 2026-08-17 09:51 IST. Do not guess. Trace the actual code path. Start from the event ID and follow the complete execution flow: shadow_mode watch → signal detector → ema_crossover.v2 → trade plan generation → ops_decisions persistence I need a forensic investigation. Find: 1. Which function generated the event. 2. Which function decided BUY_CE. 3. The exact file and line numbers. 4. The complete candle data used for the decision. 5. The exact EMA values: - previous EMA9 - previous EMA20 - current EMA9 - current EMA20 6. Determine whether a true crossover occurred. Use the actual candles from 2026-08-17 09:51 IST. 7. Show the exact boolean expression that evaluated to TRUE. For example: (previous_ema9 <= previous_ema20) AND (current_ema9 > current_ema20) or whatever expression the strategy actually uses. 8. Print every intermediate value that participated in the decision: - spot - close - EMA9 - EMA20 - VWAP - volume - trend - confidence - signal strength - quality score 9. Explain why the strategy generated BUY_CE even though the chart shows: - EMA9 below EMA20 - a bearish market structure - no visible bullish crossover 10. Do not modify any code. 11. Do not fix anything. 12. Only perform a forensic investigation and produce a report. Important: Do not rely on the TradingView screenshot. Use the exact candles that the engine actually consumed. If the engine and the chart use different candle sources, identify both sources and show the differences. Perform a complete forensic replay of trade: Event ID: sev_9fef51682bfb6ab9fedd644a8cb15018 Requirements: 1. Reconstruct every 1-minute candle from 11:20 IST until the final exit. 2. Reconstruct the entire option price path for: NIFTY18AUG2624300CE 3. Simulate the following exits: - Fixed stop (48.27) - Fixed target (70.04) - Break-even stop - EMA9 trailing stop - Swing-low trailing stop - ATR trailing stop 4. Calculate for each exit: - Entry time - Exit time - Exit price - Maximum favorable excursion (MFE) - Maximum adverse excursion (MAE) - Total profit - Profit multiple relative to initial risk (R multiple) 5. Determine which trailing model captures the highest percentage of the move. 6. Do not modify any production code. 7. Measurement and replay only. 8. Use only historical candles and historical option candles already stored in the database. Return a side-by-side comparison table. ## Daily workflow JSON files: status.json watch.json observations.json watch-summary.json ops-decisions.json market-data-validation.json attach-outcomes.json outcomes.json replay-signals.json paper-trading-report.json session-report.json replay-batch.json stop-execution-study.json quality-calibration.json quality-monitor.json shadow_evidance/console/ └── daily/ └── 2026-08-17/ └── NIFTY/ ├── status.json ├── watch.json ├── observations.json ├── watch-summary.json ├── ops-decisions.json ├── market-data-validation.json ├── attach-outcomes.json ├── outcomes.json ├── replay-signals.json ├── paper-trading-report.json ├── session-report.json ├── replay-batch.json ├── stop-execution-study.json ├── quality-calibration.json ├── quality-monitor.json └── export/ ├── all-json-files.zip └── manifest.json 18 Aug 2026: Honestly? **I agree with you.** The current logic is probably entering **too early**. Instead of: ```text EMA9 crosses EMA20 ↓ Generate signal immediately ↓ Enter trade ``` I would prefer: ```text EMA9 crosses EMA20 ↓ Wait for the crossover candle to close ↓ Wait for the next candle to close ↓ Run quality filters ↓ Generate signal ↓ Enter trade ``` That extra candle acts as a **confirmation candle** and removes many false breakouts. I would add these filters to the confirmation candle: | Filter | Condition | | ----------------- | ---------------------------------------------------------------- | | EMA confirmation | EMA9 must still be above EMA20 (BUY) or below EMA20 (SELL) | | Candle strength | Confirmation candle must close in the direction of the trade | | Volume | Volume must be greater than the previous candle | | VWAP | Price must remain above VWAP (BUY) or below VWAP (SELL) | | Candle body | Body size must be at least 50% of the candle range | | Momentum | Confirmation candle must not be a doji | | Breakout | Close must break the crossover candle's high (BUY) or low (SELL) | | Distance from EMA | Price must not be overextended from EMA9 | | Market direction | Index trend must still agree with the signal | | Signal quality | Calculate a quality score before allowing the trade | --- I would also add a **signal quality score (0–100)**. ```text EMA confirmation +20 Volume confirmation +15 VWAP confirmation +15 Momentum confirmation +15 Breakout confirmation +15 Trend confirmation +20 Total = 100 ``` Then classify signals like this: ```text 90–100 → Excellent 75–89 → Strong 60–74 → Acceptable <60 → Reject ``` --- However, **I would not change the production strategy yet.** Before modifying the EMA logic, I would run a study on your historical sessions: ```text 2026-08-11 2026-08-13 2026-08-14 2026-08-17 ``` And compare: | Strategy | Win rate | Profit | Average MFE | Average MAE | | ---------------------------------- | -------- | ------ | ----------- | ----------- | | Current EMA crossover | ? | ? | ? | ? | | EMA + next candle confirmation | ? | ? | ? | ? | | EMA + confirmation + volume | ? | ? | ? | ? | | EMA + confirmation + quality score | ? | ? | ? | ? | If the confirmed version improves both **win rate** and **profit factor**, then I would promote it to production. **My recommendation before going live:** **Do not modify the existing strategy.** Add a **new strategy** instead: ```text 9 EMA Crossover (Legacy) 9 EMA + Confirmation Candle (Experimental) 9 EMA + Quality Filter (Experimental) ``` Then replay all historical sessions and compare the results side by side before replacing the current strategy. ## Persistent project decision going forward Absolutely. I'll treat this as a **persistent project decision going forward.** **Saved strategy policy:** ```text EMA Strategy (Future Production Direction) 1. EMA9 crosses EMA20. ↓ 2. Do NOT generate a signal immediately. ↓ 3. Wait for the crossover candle to CLOSE. ↓ 4. Wait for the NEXT candle to CLOSE. ↓ 5. Run quality filters. ↓ 6. Generate the signal only if the confirmation passes. ↓ 7. Enter the trade. ``` **Required quality filters:** ```text ✓ EMA confirmation - EMA9 must still be above EMA20 for BUY. - EMA9 must still be below EMA20 for SELL. ✓ Confirmation candle direction - Candle must close in the trade direction. ✓ Volume confirmation - Volume must be greater than the previous candle. ✓ VWAP confirmation - BUY → price above VWAP. - SELL → price below VWAP. ✓ Candle body strength - Candle body ≥ 50% of the total candle range. ✓ Momentum filter - Reject doji candles. ✓ Breakout confirmation - BUY → confirmation candle closes above the crossover candle high. - SELL → confirmation candle closes below the crossover candle low. ✓ EMA extension filter - Reject trades that are too far from EMA9. ✓ Market trend confirmation - Index direction must agree with the trade direction. ✓ Signal quality score - Calculate a final quality score before allowing the trade. ``` **Signal-quality classification:** ```text 90–100 → Excellent 75–89 → Strong 60–74 → Acceptable <60 → Reject ``` **Implementation policy:** ```text Keep the current strategy unchanged. Add these as separate strategies: | Strategy | Description | | --------------- | ------------------------------------------ | | 9 EMA Crossover | Your existing strategy (unchanged) | | 9 EMA Confirmed | Wait for the next candle to close | | 9 EMA Quality | Next-candle confirmation + quality filters | ``` **Validation policy before production:** Every strategy change must be replayed against historical sessions and compared using: * Win rate * Profit factor * MFE * MAE * Trailing-SL behavior * Total P&L * Signal quality distribution We'll follow this approach for future reviews and before promoting any EMA changes to production. Honestly? **I think you're right.** At this stage, **I would not create a new strategy.** The more I think about your current architecture, the more I think that creating **9 EMA Confirmed** as a separate strategy would create a lot of unnecessary work. You already have: * Super Admin → Strategy Registry * Scanner * Index Scanner * Shadow Mode * `observe` * `watch` * `attach-outcomes` * `outcomes` * `replay-signals` * `generate_paper_trading_report` * `quality-calibration` * `quality-monitor` * 15 Daily Workflow JSON files * AutoPaper * Reports * Historical replays A new strategy would force you to touch almost every layer of the platform. --- ## I would make only two changes. ### 1. Improve the existing 9 EMA Crossover entry logic. Current logic: ```text EMA crossover ↓ Immediate signal ↓ Immediate entry ``` Proposed logic: ```text EMA crossover ↓ Wait for the next candle ↓ Wait for the next candle to CLOSE ↓ Run quality filters ↓ Generate signal ↓ Enter trade ``` --- ### 2. Fix the risk model. Based on the 17-Aug-2026 root-cause analysis: ```text Current stop: entry × 2% ↓ 0.985 premium points ↓ 0.36 ATR ↓ Stop hit almost immediately ``` The report identified this as the **first incorrect calculation**. Your own investigation showed: | Configuration | Result | | ---------------------- | ------ | | Current implementation | -0.985 | | Trail-only changes | -0.985 | | Replay changes | -0.985 | | 1×ATR risk unit | +5.21 | The replay engine wasn't wrong. The trailing stop wasn't wrong. The entry timing wasn't wrong. The **risk unit was wrong.** --- ## If this were my production roadmap, I'd do it in this order. ### Phase 1 (tomorrow) **Improve entry quality.** * Add confirmation candle. * Add volume confirmation. * Add candle body strength. * Reject weak candles. * Reject sideways markets. * Add `signal_quality`. * Add `would_reject`. --- ### Phase 2 (after 1-2 weeks of data) **Fix risk management.** Do not touch the risk model after analyzing **one trading session**. Collect: * `replay-signals.json` * `paper-trading-report.json` * `ops-decisions.json` * `quality-monitor.json` * `stop-execution-study.json` for at least **5-10 sessions**. Then analyze: * Average MFE * Average MAE * Median option candle range * Average holding time * Stop-hit distribution Only then should you redesign the stop model. --- ## The biggest advantage of modifying the existing strategy: **Nothing in your Daily Workflow changes.** Your existing workflow remains: ```text Status ↓ Watch ↓ Observations ↓ ops_decisions ↓ Backfill Today ↓ attach-outcomes ↓ outcomes ↓ replay-signals ↓ generate_paper_trading_report ↓ quality-calibration ↓ quality-monitor ↓ Export JSON ``` All 15 JSON files continue to work exactly as they do today. My recommendation: **Do not create a new strategy.** **Refine the existing EMA Crossover strategy and fix the risk model after you collect more sessions of evidence.** # The strategy rejects signals when: Candle body < 0.50 Price doesn't exceed the crossover candle's high EMA distance exceeds 0.70% Other quality gates fail ================================================== STORE THESE ADDITIONAL FIELDS ================================================== Extend the signal payload to store: - crossover_timestamp - confirmation_candle_timestamp - signal_timestamp - confirmation_candle_open - confirmation_candle_high - confirmation_candle_low - confirmation_candle_close - confirmation_candle_volume - signal_quality - would_reject replay-signals.json attached. Please ack ops-decisions.json attached. Please ack paper-trading-report.json attached. Please ack session-report.json attached. Please ack index_candle.json attached. Please ack options_contracts.json attached. Please ack options_candles.json attached. Please ack 19 Aug 2026: Shadow command poll candle miss capture issue resolved: 1. Root cause Three lines, all pre-change (git show HEAD): File Line What it does capture.py:243 payload = observe(symbol, instrument_type, store=store) one poll → one observation runner.py:82 + runner.py:93 build_analysis_frame(…, observed_at) → base["bar_ts"] = witness.last_closed_bar_ts the frame is built at the clock, and only the tail bar becomes a decision point. Every earlier bar in the 200-bar window is EMA history and is never itself evaluated capture.py:255 do_sleep(_seconds_to_next_bar(read_clock(), interval_seconds)) the sleep is aligned to the next bar boundary, computed from the clock read after the observation The three compose into the defect: an observation that overruns its minute does not merely arrive late — the next sleep targets the following boundary, and the bar in between is stepped over with nothing to catch it. The 2026-08-19 NIFTY evidence shows exactly that signature — whole polls missing, the surviving polls still landing on :05: bar 13:04 | observed 13:05:05 bar 13:12 | observed 13:13:05 bar 13:05 | observed 13:06:05 bar 13:14 | observed 13:15:05 ← 13:14:05 poll never ran bar 13:07 | observed 13:08:05 bar 13:16 | observed 13:17:05 ← 13:16:05 poll never ran Measured over the session: 370 stored candles, 295 frames, 75 stored bars never evaluated, including 13:13, 13:42, 13:44. ops-decisions.json reported observations: 295 and nothing about the 75. 2. Files modified File Why data/market_sessions.py SessionShape.classify() + 3 verdict constants — derived from the existing three populations, so eligibility has one definition and both consumers read it shadow_mode/coverage.py (new) pure catch-up planner: plan_catch_up, catch_up_depth, settled_at. No I/O, no clock, no strategy shadow_mode/capture.py observe_pending (the catch-up poll), _PollFeed/_AsOfGateway, watch carries a cursor. observe_once unchanged shadow_mode/store.py bar_recorded() (dedup before analysis) and latest_recorded_bar() (restart cursor, read from the evidence itself) shadow_mode/record.py, runner.py capture_mode provenance field — additive, read by nothing shadow_console/daily.py the coverage block on decisions cli.py docstrings only 3. Catch-up algorithm poll(now, cursor): depth = minutes(now − cursor) # arithmetic, before any fetch fetch = gateway.fetch_1m(200 + depth) # ONE call, whatever the gap closed = rows where bar + 60s <= now # forming bar refused here too bars = closed ∩ session_date(newest) , classify == eligible , > cursor for bar in bars (oldest → newest): skip if store.bar_recorded(identity(bar)) # indexed, pre-analysis run_shadow_analysis(now=bar+60s+5s, gateway=feed.as_of(bar)) record_once(decision) Four properties worth naming: depth sizes the fetch. Truncating a 200-bar window at a bar 20 minutes back leaves 180, the pipeline refuses below 200, and the row would say insufficient_data — a fabricated absence. Deliberately not bounded by the per-poll evaluation limit: the oldest planned bar is one after the cursor whatever the limit is. One fetch, one chain. 362 frames cost 1 candle call + 1 chain call, and all are cut from one snapshot, so a bar arriving mid-catch-up cannot make two frames disagree about history. observed_at is the bar's own (bar + 60s + BAR_SETTLE_SECONDS); the actual write time goes to the existing ops_decisions.captured_at. Restart resumes from ops_decisions via latest_recorded_bar — no new table, no checkpoint file, no migration. 4. Session / CAS eligibility SessionShape.classify(minute), derived from the populations already in the model: Verdict Index, post-CAS Action eligible 09:15–15:14 continuous + 15:15, 15:28, 15:29 discretionary evaluate structurally_empty 15:16–15:27 excluded, reported by name outside_session anything off the grid excluded The live run evaluated six of 2026-08-19's eight 15:20–15:27 prints. The corrected observer excludes all eight and says so. 5. Sample coverage JSON — the real 2026-08-19 session "coverage": { "available": true, "session_model": "cas", "eligible_closed_bars": 362, "evaluated_frames": 288, "duplicate_frames": 0, "missing_eligible_bar_timestamps": ["09:15","09:21", … ,"13:13", … ,"13:42","13:44", … ,"15:29"], "excluded_bar_timestamps": ["15:20","15:21","15:22","15:23","15:24","15:25","15:26","15:27"], "excluded_reasons": {"structurally_empty": ["15:20", …, "15:27"]}, "coverage_complete": false, "stored_index_candles": 370, "frames_total": 295, "frames_on_excluded_bars": 6, "frames_without_a_bar": 0, "frames_from_another_session": 1 } Both identities close exactly: 288 + 74 = 362 eligible; 362 + 8 = 370 stored; 288 + 0 + 6 + 0 + 1 = 295 frames. (The one frames_from_another_session is the 09:15:01 poll, which correctly saw 2026-08-18 15:29.) 6. 2026-08-19 read-only validation Stored candles only, in-memory store, nothing written to the database, historical export untouched: evaluated bars: 362 09:15 → 15:29 chronological: True unique: True excluded: 15:20 … 15:27 candle fetches: 1 5m fetches: 1 chain fetches: 1 Bar verdict direction strategy (ema_crossover.v3) says 13:13 REJECTED / no_signal BUY_PE crossover_detected=false, no_crossover_on_previous_bar; event_detected=true 13:42 REJECTED / no_signal BUY_CE same 13:44 REJECTED / no_signal BUY_PE same Read correctly: the EMA sign change printed on these bars (observational detector fires), and v3 confirms a cross printed on the bar before, so its own answer on these bars is "nothing to confirm yet". The confirmation bars it would have read: 13:14 crossover_detected=true, 13:43 G3 confirmation_candle_direction_mismatch, 13:45 G4 confirmation_body_too_weak. No claim is made that any of these should have been a trade. The harness carries no option chain and no paper book, so anything after the strategy (dedup_context_unavailable) is a property of the harness, not a finding. 7. Tests tests/test_shadow_coverage.py — 48 passed, covering all eight required validations: continuous polling (one bar, once), 2-/3-/7-minute and multi-hour gaps caught up chronologically, repeat polling, restart/resume, the in-progress candle, structurally-invalid CAS bars, the coverage counters, and per-bar equivalence over 27 fields (outcome, verdict, rejection_reason, crossover_diagnostics, gate_diagnostics, filter_verdicts, risk_verdicts, features, event id …) between uninterrupted and caught-up processing of the same sequence — with a fixture assertion that a crossover actually occurs in the compared window. Plus 8 new classify tests in test_market_sessions.py. 8. Full CI bash backend/scripts/ci/run_all.sh ════ summary ════ passed 15 tests v2-isolation v2-importlinter v2-config-schema-sync operations-isolation research-isolation research-engine schema-evolution research-archive-integrity dataset-programme research-platform signal-validator developer-command-reference v2-coverage operations-coverage OK — every gate passed. (2,035 → full suite green; developer-command-reference regenerated in the same change.) 9. Confirmation No strategy behaviour, gate, threshold, risk model, exit/trailing-stop, option selection, replay semantics or paper-trading logic was changed. v2/strategy/, v2/filters/, v2/risk/, v2/positions/, replay/ and the broker layer are untouched — the diffstat above is the whole change. The engine composition per frame is byte-identical; the only difference is which bars are handed to it and which series the gateway serves for each. No migration, no new table, no new CLI command; ops_decisions gets one real row per eligible bar via the existing identity, and JSON is additive only. Prompt: URGENT INVESTIGATION — DO NOT MODIFY ANY TRADING LOGIC. The objective is ONLY to identify every gap between the actual EMA crossover event and the recorded signal. Response: ROOT-CAUSE INVESTIGATION — the single 2026-08-19 signal No file was modified. Everything below was produced by read-only queries and by re-running pure functions (describe(), detect_ema_event()) over stored candles in scratchpad scripts. The git status above is unchanged from the coverage task; nothing in it belongs to this investigation. Source of truth for the signal: ops_decisions row id=1294, trade_plan_id=sev_438fda328e544f3f18471e51c74735d7, as exported to ops-decisions_shadow_signals_NIFTY_2026-08-19.json. All eight expected values confirmed: bar 12:08 IST, observed 12:09:05.012896 IST, BUY_PE, spot 24058.15, NIFTY25AUG2624000PE, entry 75.30, stop 73.794, target 100.33711115983587. 1. Which exact candle triggered the crossover? The 12:07 candle. ema_crossover.v3 tests the pair at indices [-3, -2] — ema_crossover_v3.py:620-644 — so on a frame whose tail is 12:08, the cross candle is 12:08 − 1 = 12:07. EMA9 EMA20 gap (9−20) previous candle 12:06 ([-3]) 24067.582146 24067.555492 +0.026654 crossover candle 12:07 ([-2]) 24066.585716 24067.083540 −0.497824 confirmation candle 12:08 ([-1]) 24064.908573 24066.237489 −1.328915 Sign flips + → − between 12:06 and 12:07 → 9ema_20ema_bearish_cross → BUY_PE. (Recomputed from stored candles; the live row's own features give ema9 24064.89809196826 / ema20 24066.235417140902 at 12:08 — see §11 item 1 for why they differ slightly.) Note there were two crossovers in three minutes: a bullish one printing on 12:06 (gap +0.0267 — 2.7 hundredths of a point) and the bearish one on 12:07. The bullish one was refused on the next bar at G1 (confirmation_ema_side_lost) — it had already unwound. 2. Which candle was the confirmation candle? 12:08 IST, index [-1] (ema_crossover_v3.py:674). field value timestamp 2026-08-19 12:08:00 IST open / high 24064.9 / 24064.9 low / close 24056.2 / 24058.2 (live close 24058.15) volume 0.0 — an index is not a traded instrument range 8.7 body 6.7 → body_ratio 0.7701 atr_14 6.6081 (live: 6.5973942450160425) → range_atr 1.3166 3. 12:08 close, 12:09 open, or another candle? The 12:08 close. The 12:09 candle played no part whatsoever in the decision. capture.watch loop, 12:09:05 IST └ capture.observe_once capture.py:128 └ runner.run_shadow_analysis runner.py:43 now = 12:09:05.012896 └ build_analysis_frame v2_frame_builder.py:380 └ gateway.fetch_1m_candles(NIFTY, 200) └ drop_forming_bars(interval=60, now) ← removes the forming 12:09 bar └ build_market_frame v2/pipeline.py:228 spot = candles_1m[-1].close = 24058.15 └ engine.run(request, frame) engine.py:285 frame.spot == 24058.15 == the 12:08 close is the proof: the frame's tail is 12:08. 4. event_detected=false and status=shadow_signal / outcome=SIGNAL / verdict=proposed Expected under v3, not a contradiction — the two detectors read different bars, one apart. predicate on the 12:08 frame detect_ema_event (events.py:101-109) sign change between [-2] and [-1] = 12:07 → 12:08 gap −0.498 → −1.329, no sign change → None ema_crossover.v3 (ema_crossover_v3.py:620-644) sign change between [-3] and [-2] = 12:06 → 12:07 +0.027 → −0.498, sign change → BUY_PE detect_ema_event is ema_crossover.v2's predicate — v2 signalled on the bar the cross printed on; v3 signals on the bar after. The execution path in runner.py:257-281: event = detect_ema_event(frame) → None → event_detected=False, event=null result = engine.run(...) → verdict="proposed" if verdict == "proposed": outcome = OUTCOME_SIGNAL ← taken; the event branch is never reached store._STATUS_BY_OUTCOME[SIGNAL] = "shadow_signal" Verified against the adjacent rows — the two detectors are exactly one bar apart, as designed: bar 12:07 | shadow_reject | event_detected True | v3: crossover_detected True, refused G1 bar 12:08 | shadow_signal | event_detected False | v3: crossover_detected True, CONFIRMED 5. The complete pipeline market data brokers → v2/data/gateway_impl._fetch_1m_candles → drop_forming_bars(60, now) api.v2_frame_builder.build_analysis_frame:409 _load_cache → FeatureRegistry/register_from_names → v2.pipeline.build_market_frame _session_bounds (24172.8/24027.9) → stage4_features_from_store → _option_chain_feature crossover det. v2.strategy.ema_crossover_v3.describe:604 _compute_ema_series(closes, 9|20) test on ema9_series[-3:-1] vs ema20_series[-3:-1] → BUY_PE §A.2 trend veto (Neutral → passes) :662-671 confirmation _ohlcv(candles[-1]) / _ohlcv(candles[-2]) :674-697 gate evaluation G1…G9 in order, short-circuit on first failure :736-811 _volume_confirmation → "unavailable" (G5 abstains) :926 signal proposal _compute_strength → 0.57 ; _confirmation_quality → 53.8 :828-847 Signal(direction, reason, strength, features_used, v3) :911 engine.run:322 → evaluate_filters_ordered (7 filters) :333 → evaluate_risk_rules (2 rules) :366 option selection engine._assemble_entry_plan:469 → assemble_entry_plan_for_options:228 → select_contracts (ATM window, liquidity) → NIFTY25AUG2624000PE, ltp 75.30, liquidity_score 87.9 stop calculation assemble_entry_plan:92 75.30 × (1 − 0.02) = 73.794 target calc. _runner_target:200 75.30 + 25.0371 = 100.33711115983587 persistence engine._proposed_result:579 → runner._decide:234 → ShadowDecision shadow_mode.identity.identify → sev_438fda32… (trade_plan_id) shadow_mode.store.record:83 → ops_decisions (evidence_source=shadow_frame) JSON export operations.shadow_signals.display/export_document → ops-decisions_shadow_signals_*.json shadow_console.daily._decisions → ops-decisions.json 6. Every gate Reproduced by re-running ema_crossover_v3.describe() on the reconstructed 12:08 frame — outcome confirmed, no refusal: Gate Rule Threshold Measured Result G1 EMA-9 still on the trade's side of EMA-20 (sign test) gap at confirmation = −1.328915 (< 0 for BUY_PE) passed G2 close beyond EMA-9 (sign test) 24058.2 < 24064.9086 passed G3 candle direction (sign test) close 24058.2 < open 24064.9 passed G4 body / range, not a doji 0.50 0.7701 passed G5 volume ≥ trailing mean 1.0 volume_state="unavailable", ratio None abstained G6 EMA gap must widen (sign test) cross_expansion +0.831092 passed G7 range ≥ 0.588 × atr_14 0.588 8.7 / 6.6081 = 1.3166 passed G8 close breaks cross bar's low (sign test) 24061.3 − 24058.2 = +3.1 passed G9 |close − ema9| / ema9 0.70 % 0.0279 % passed Pre-gate stages: trend-contradiction veto passed (market_trend='Neutral' — Neutral cannot fire it). Post-gate: strength blend 0.57 (slope down); v3's own confirmation quality 53.8, band reject, would_reject True (components: ema_confirmation 7.5, momentum 18.8, breakout 12.5, candle_size 15.0). See §11 item 3 — that number is on no artefact. 7. Stop loss = 73.794 entry_price (contract ltp) = 75.30 threshold.entry_stop_loss_pct = 0.02 v2/config/resolved.ENTRY_STOP_LOSS_PCT stop = entry × (1 − 0.02) = 75.30 × 0.98 = 73.794 entry_plan.py:92 risk (1R) = 75.30 − 73.794 = 1.506 ATR is not used in the stop. It is a flat 2 % of the premium. 8. Target = 100.33711115983587 reward_risk_ratio is not supplied, so the 1R target (76.806) is replaced by the runner ceiling (entry_plan.py:200): atr_14 = 6.5973942450160425 (the underlying's ATR, live frame) ATM_OPTION_DELTA = 0.5 RUNNER_TARGET_ATR_MULT = 7.59 ceiling_distance = 6.5973942450160425 × 0.5 × 7.59 = 25.03711115983588 target = 75.30 + 25.03711115983588 = 100.33711115983587 ✓ exact Guard passed: 25.037 > risk 1.506, so the ceiling replaced the 1R target. 9. Why the position closed at 12:14 Trailing stop, four promotions, then the hard stop read the promoted level. Contract candles are historical_option_candles for NIFTY25AUG2624000PE: bar O H L C (mark) stop in force stop after moved 12:09 75.45 78.00 75.00 77.25 73.794 75.744 +1.95 12:10 77.05 78.50 76.05 76.25 75.744 75.744 — 12:11 76.25 77.85 76.10 77.70 75.744 76.194 +0.45 12:12 77.70 78.50 77.40 77.90 76.194 76.394 +0.20 12:13 77.70 79.70 77.35 79.60 76.394 78.094 +1.70 12:14 79.50 79.50 77.40 78.00 78.094 78.094 CLOSED Initial stop 73.794; every promotion is mark − 1R (77.25−1.506 = 75.744; 77.70−1.506 = 76.194; 77.90−1.506 = 76.394; 79.60−1.506 = 78.094). All four applied: true, 0 refused, all by position_policy_trailing_stop, activated on the entry bar (12:09) because the mark was already +1.95 ≥ 1R. At 12:14 the bar's low 77.40 breached the stop 78.094 → position_policy_hard_stop_loss.v2 closed at 78.094 (the level, per the v2 intrabar model), reason hard_stop_loss_triggered, classification trailing stop, state STOPPED. Result +2.794 premium points in 5 bars. MFE +4.30, MAE −0.95, capture efficiency 0.6498. Target 100.337 was never approached (contract's session high 83.75). 10. Timestamp comparison Source Signal bar Observation Entry Exit ops_decisions (DB) bar_ts 06:38:00Z = 12:08 IST generated_at 06:39:05Z = 12:09:05 IST; captured_at 06:39:07Z — — Signal JSON export same same — — v3.describe() derived crossover_time 12:07:00+05:30, confirmation_candle_time 12:08:00+05:30, signal_time 12:09:05.012896+05:30 replay-signals.json signal.bar_ts 06:38:00Z observed_at 06:39:05Z entry.ts 12:09:00+05:30 exit.ts 06:44:00+00:00 = 12:14 IST paper-trading-report.json — — entry_ts 12:09:00+05:30 exit_ts 06:44:00+00:00 TradingView not accessible from this environment — compare against 12:07 (cross) and 12:08 O 24064.9 / H 24064.9 / L 24056.2 / C 24058.15 All references resolve to the same candles: cross 12:07, confirmation 12:08, entry bar 12:09, exit bar 12:14. Two rendering defects: (a) replay-signals.json and the paper report emit entry.ts in IST and exit.ts / every stops.movements[].ts in UTC — inside the same object (analytics.holding holds entry_ts +05:30 beside exit_ts +00:00); (b) crossover_time / confirmation_candle_time are derived from frame.now, not read from the store (_bar_timestamps, candle_timestamp_basis says so). 11. Every inconsistency found The live frame and the stored candles are different data for the same minute. Measured over all 294 same-day frames: live spot equals the stored bar's close in only 59 cases (20.1 %), yet falls inside that same bar's [low, high] in 293 (99.7 %), and inside the next bar's range in 0. Median |diff| 0.53 pt, mean 1.97, max 97.35. It matches the bar's open in 0 %. At 12:07 the live frame carried spot 24064.45 while the store's 12:07 closes at 24062.6. Signature: the live bar the engine decided on is a mid-bar value of that minute, and the settled bar arrives later via backfill. historical_candles OHLC columns are MySQL FLOAT (single precision) and the client returns them through a 6-significant-digit decimal string: 24058.15 is stored as 24058.15039063 and read back as 24058.2. On top of (1), the reconstructed EMAs differ from the live ones by up to 0.195 (12:07). The 12:06 crossover's gap was +0.0267 — an order of magnitude inside that divergence, so replay and live can disagree about whether a crossover happened at all. v3's own confirmation-quality verdict is not persisted. The strategy scored 53.8 → band reject, would_reject=True. The stored row carries only signal_quality_score: 73 / quality_would_reject: false — v2/filters/signal_quality.v2's, a different score by design (v3 docstring §"signal_quality, its band"). runner._decide stores _scalar_features(frame.features) and drops Signal.features_used entirely, so the reject-band verdict exists nowhere. The paper report shows would_reject: false for a signal the strategy itself banded reject. 16 of the 17 rejections on 2026-08-19 carry a fabricated reason. The export's rejection_reasons is {"strategy_trend_veto": 16, "m4_session_exhaustion": 1}. Verified on the 12:07 row: stored reason strategy_trend_veto; re-running describe() on that frame returns confirmation_ema_side_lost (G1). The trend veto could not have fired — market_trend was Neutral, which by construction passes it. runner._strategy_reason guessed the reason before commit fef5a1e (2026-08-19 18:49 IST, nine hours after this signal); the guess is already replaced in code, but every 2026-08-19 stored row predates it. crossover_diagnostics is absent from every 2026-08-19 row — same chronology. The nine-gate table in §6 is a reconstruction, not a read. confirmation_lag is structurally null on every v3 signal. runner.py:258 computes it only if event is not None, and detect_ema_event is false on every v3 confirmation bar by construction (§4). A profitable trade is reported as LOSS with win_rate: 0.0. The paper report shows final_outcome: LOSS, pnl: +2.794, profitable_trades: 1, win_rate: 0.0. Cause: the outcome layer models the plan's ratified levels with no trail, and the premium fell to 73.2 at 12:19 — below the original 73.794 — while never reaching 100.337. The report's divergence block states this. Both figures are right in their own model; the summary header is misleading. The plan's entry price is one bar older than its entry timestamp. Entry premium 75.30 is exactly the 12:08 option bar's close; the replay stamps the fill at 12:09, a bar that opened 75.45 and traded to 78.00. Internally consistent (the whole frame is as-of the 12:08 close) but a ~65-second decision-to-execution gap that a live order would pay. Mixed IST/UTC rendering in replay-signals.json and the paper report (§10). The observer never evaluated 75 of the session's 370 stored bars (the task completed immediately before this one). The 12:05 / 12:07 crossover neighbourhood was evaluated, so this signal is unaffected — but the session's crossover census is not complete. CONFIRMED BUGS # Bug Evidence C1 Live decision frames are not reproducible from stored candles. Only 20.1 % of 294 live spots equal the stored close of the same bar; 99.7 % are mid-bar values of it. Median 0.53 pt, max 97.35 pt. §11.1 C2 historical_candles OHLC stored as FLOAT, read back rounded to 6 significant digits (24058.15 → 24058.2). §11.2 C3 Stored rejection reasons are fabricated for 16 of 17 rejections on 2026-08-19 — strategy_trend_veto named a gate that never fired; the true 12:07 reason is confirmation_ema_side_lost (G1). Already fixed in code by fef5a1e; the stored evidence is wrong. §11.4 C4 v3's confirmation-quality verdict is discarded. Signal.features_used never reaches the evidence row; the signal that v3 banded reject (53.8) is recorded as would_reject: false. §11.3 C5 confirmation_lag is unconditionally null for every v3 signal — gated on a v2-era detector that cannot fire on a v3 confirmation bar. §11.6 C6 Mixed IST/UTC timestamps inside one JSON object in replay-signals.json and paper-trading-report.json. §10, §11.9 C7 win_rate: 0.0 on a session whose only trade returned +2.794 — two exit models summed into one header. §11.7 event_detected=false beside SIGNAL is NOT a bug. It is the documented v2/v3 one-bar offset (§4), already described in shadow_console/daily.py's detector_note. The field name is stale vocabulary, not a defect in the decision. SUSPECTED BUGS # Suspicion What would confirm it S1 The broker's intraday 1-minute candle is published before the minute is complete and later revised, so the engine confirms crossovers on partial bars. Fits: 0 % match the open, 20 % the close, 99.7 % lie between. Poll the broker for bar T at T+5 s, T+30 s and T+10 min and diff the OHLC. S2 The option-chain ltp is one bar stale — 75.30 is exactly the 12:08 option close, returned at 12:09:05 after the 12:09 bar had traded 75.45→78.00. Timestamp the chain snapshot; compare ltp against the contract's live tick. S3 Crossover detection is not robust to the data divergence. The 12:06 cross had a gap of +0.0267 against a measured live-vs-stored divergence of up to 0.195. Re-run the session's crossover census on both datasets and count sign disagreements. S4 The trail activating on the entry bar (mark already +1.95 = 1.29R at the first mark) may make the runner ceiling structurally unreachable. Measure activation-bar distribution across the corpus. FILES RESPONSIBLE File Findings backend/replay/models.py (HistoricalCandle column types) + migration history C2 backend/data/index_scanner.py _fetch_1m_candles, backend/v2/data/gateway_impl.py C1, S1 backend/shadow_mode/runner.py C3 (fixed), C4, C5 backend/shadow_mode/record.py C4 backend/shadow_mode/replay_signals.py C6 backend/generate_paper_trading_report/ (assembly) C6, C7 backend/data/option_chain.py, backend/v2/signal_engine/option_selector.py S2 backend/v2/strategy/ema_crossover_v3.py (no defect found — reproduced exactly) FUNCTIONS RESPONSIBLE v2/data/gateway_impl._fetch_1m_candles / drop_forming_bars — C1, S1 replay/candle_store._row_to_candle (reads a FLOAT column) — C2 shadow_mode/runner._strategy_reason — C3 shadow_mode/runner._decide + _scalar_features — C4 (drops signal.features_used) shadow_mode/runner._confirmation_lag (guarded by if event is not None) — C5 shadow_mode/replay_signals._iso / _exit / _analytics — C6 generate_paper_trading_report summary assembly — C7 api/v2_frame_builder._option_chain_feature → gateway.fetch_option_chain — S2 REQUIRED FIXES — descriptions only, no code changed C1/S1 (blocker for go-live). Establish which dataset is authoritative for a minute before trading live. Verify broker candle-revision behaviour, then either delay the observation until the bar is provably settled, or persist the live frame's candles alongside the decision so a replay can reproduce it exactly. Until then, no backtest or replay figure is a statement about what the live engine would have done. C2. Migrate historical_candles OHLC from FLOAT to a decimal type with explicit scale (a migration under backend/db/migrations/, per the schema-evolution rule), and re-ingest. Precision loss is currently the same order as the smallest crossover gaps. C3. No code change needed (fef5a1e fixed it). The 2026-08-19 stored reasons should be annotated as pre-fix, never rewritten — closed evidence is corrected by appending, not editing. C4. Persist Signal.features_used (or at minimum signal_quality, signal_quality_band, signal_quality_owner, would_reject) onto the evidence row, and label the two quality scores by owner everywhere they are rendered. Today the strategy's own reject verdict is unrecoverable. C5. Re-anchor the confirmation-lag measurement to the strategy's own crossover bar rather than to detect_ema_event, or record explicitly that it is not measurable under v3 instead of emitting null. C6. One timezone per document. Render every timestamp in IST with an explicit offset, as the entry already is. C7. Report the two exit models as two labelled columns; never publish a win_rate that pools them. A LOSS with pnl > 0 must not reach a summary header. S2. Timestamp the option-chain snapshot and carry the age of the ltp on the plan, so a stale premium is visible rather than inferred. S3. Before go-live, re-run the crossover census on live-captured vs stored candles and publish the disagreement count. A strategy whose trigger sits inside the data's own noise band needs that number stated. Done. I have saved this as the **current NS Algo Trade project checkpoint and priority.** Going forward, I will treat this as the active state of the project until we explicitly close these items. # 🚫 GO-LIVE CHECKLIST (DO NOT PROCEED TO OTHER STRATEGIES YET) ## 🔴 P0 — MUST BE FIXED BEFORE GO-LIVE ### **C1 — Live candle pipeline defect (CRITICAL)** **Problem** The live engine is making decisions from a candle that may have been cached while it was still forming. **Evidence** - Live EMA crossovers: **25** - Stored EMA crossovers: **23** - Shared crossovers: **20** - Live-only crossovers: **5** - Stored-only crossovers: **3** **Impact** - Different EMA values - Different crossover events - Different signals - Different strike selection - Different trading decisions **Files** ```text backend/data/index_scanner.py backend/v2/data/gateway_impl.py ``` **Functions** ```text _fetch_1m_candles() collect_1m_candles() drop_forming_bars() ``` **Status** 🚫 BLOCKER --- ### **N1 — The same defect exists in the production scan API** **Problem** The bug is not limited to Shadow Mode. The same candle path is used by the production analysis endpoint. **Affected path** ```text /api/v2/analyse ``` **Flow** ```text v2_routes.py ↓ v2_frame_builder.py ↓ gateway_impl.py ↓ index_scanner.py ``` **Impact** A paying user could receive a signal generated from an incomplete candle. **Status** 🚫 BLOCKER --- ### **N3 — The readiness validator cannot detect the candle mismatch** **Problem** The system reports: ```text Ready = 100% ``` But it never compares: ```text Live candles vs Stored candles ``` **Current behavior** ```text Readiness check ↓ Row count validation only ``` **Missing validation** ```text EMA comparison Crossover comparison Signal comparison Spot comparison ``` **File** ```text backend/replay/session_readiness.py ``` **Status** 🚫 BLOCKER --- # 🟠 P1 — FIX AFTER THE P0 ITEMS ### **C3 — Rejection reasons are incorrect** **Problem** Strategy-stage rejections are being stored incorrectly for historical sessions. **Files** ```text backend/shadow_mode/runner.py backend/shadow_mode/events.py backend/session_report.py ``` --- ### **C4 — `features_used` is being discarded** **Problem** Important strategy evidence is never stored. Missing fields include: ```text EMA9 previous EMA20 previous EMA9 current EMA20 current Crossover timestamp Confirmation timestamp Confirmation candle Signal quality components ``` **File** ```text backend/shadow_mode/runner.py ``` --- ### **N2 — The 15:20 bar is distorting reports** **Problem** One abnormal candle is dominating session statistics. **File** ```text backend/session_report.py ``` --- # 🟢 P2 — TECHNICAL DEBT / COSMETIC ### **C2 — FLOAT → DECIMAL migration** **Status** Not a trading blocker. --- ### **C6 — Mixed IST/UTC timestamps** **Status** Rendering issue only. --- ### **C7 — `win_rate` vs `profitable_trades` labeling** **Status** Reporting terminology issue. --- ### **N4 — Calendar model mismatch** **Status** Low priority. --- # 📌 PROJECT RULE (NOW SAVED) Until the following three items are fixed: ```text C1 N1 N3 ``` We will **NOT**: - Enable additional strategies - Move to production - Shift focus to new features - Start work on other trading logic We will treat the **live candle pipeline investigation as the highest-priority task** until it is resolved. My updated go-live sequence Priority Task Status P0 Verify actual broker candle behavior ❌ P0 Confirm cache as the root cause ❌ P0 Fix forming-candle caching ❌ P0 Audit historical_candles contamination ❌ P0 Add live-vs-historical validation tests ❌ P0 Fix readiness validation ❌ P1 Fix rejection diagnostics ⏳ P1 Persist strategy diagnostics ⏳ P2 Fix timestamp rendering ⏳ P2 Improve report labels ⏳ P3 FLOAT migration ⏳ Auto start AI scan by setting future time during market hours. At a time only 1 auto scheduler can set. Production audit: investigate why ema_crossover is generating unusually small stop losses. Example trade: Observed: 2026-08-20 12:09:05 IST Signal candle: 12:08:00 Direction: BUY_PE Spot: 24058.15 Strategy: ema_crossover Strength: 0.57 Market trend: Neutral Trend confidence: 20.70 Quality: 73 Contract: NIFTY25AUG2624000PE Entry: 75.30 Stop: 73.79 Target: 100.34 The stop distance is only 1.51 points (2.0% of premium), while the target distance is 25.04 points, producing a 1:16.6 risk-reward ratio. Perform a complete trace. Do not assume the stop is wrong. Prove it. Audit the entire execution path: 1. Find the exact code path: signal generation → ema_crossover strategy → option selection → entry calculation → stop calculation → target calculation → JSON export → shadow_mode storage → UI rendering 2. Identify the exact function that generated: - Entry = 75.30 - Stop = 73.79 - Target = 100.34 3. Print every intermediate variable used in the calculation: - Signal candle OHLC - Confirmation candle OHLC - Index ATR - Option ATR - EMA9 - EMA20 - Spread - Premium - Strike - Contract candle OHLC - Previous candle low - Previous swing low - Volatility multiplier - Risk multiplier - Reward multiplier 4. Recalculate the stop manually from the raw candle data. 5. Compare: calculated stop vs stored stop vs exported stop vs UI-rendered stop 6. Verify whether the stop is based on: - Candle low - Swing low - ATR - Percentage - Fixed points - Option premium structure - Index structure 7. Search for: - min() - max() - clamp() - round() - floor() - ceil() inside every stop-loss function. 8. Check whether stop values are being rounded after the calculation. 9. Check whether the stop uses: entry_price - x instead of: structure_low - buffer 10. Generate a table: | Variable | Actual value | | --- | --- | | Entry | | | Stop | | | Risk | | | Risk % | | | Target | | | Reward | | | RR ratio | | | ATR | | | Signal candle low | | | Confirmation candle low | | | Swing low | | 11. If a bug exists: - Identify the exact line number. - Explain why the stop became 73.79. - Propose the fix. - Add regression tests. - Verify that the fix does not change previously validated trades. Do not modify any code until the root cause is proven. Use real production data only. 21 Aug 2026: I reviewed the full journey we have built so far: 1. Shadow Mode stability fixes 2. Shadow Signal → Paper Trading handoff architecture 3. Paper Position Monitor ownership isolation 4. User broker isolation 5. Ownership enforcement 6. Repository access intent hardening My answer: **we are very close, but I would NOT call it production-live ready yet without closing a few remaining items.** The foundation is now strong. The dangerous multi-user data leaks and broker identity problems are mostly solved. But there are still a few **production-grade trading platform issues** I would fix before allowing real customers. My priority ranking: --- # Current Status ## ✅ READY / SOLVED ### 1. Shadow Mode separation Status: ✅ Good Architecture: ``` Shadow Mode | | Signal Generation Only ``` No position management inside Shadow Mode. Correct. Do not touch it. --- ### 2. Paper Trading ownership isolation Status: ✅ Good Before: ``` User A | auto_paper_positions | Everyone could see ``` Now: ``` User A | user_id=A | only User A can access ``` Good. --- ### 3. Broker isolation Status: ✅ Good Before: ``` User A broker | global SDK | User B position ``` Dangerous. Now: ``` User A Position | | User A Broker Session | | User A isolated SDK User B Position | | User B Broker Session ``` Correct architecture. --- ### 4. Repository ownership intent Status: ✅ Good decision This was an important architectural improvement. Before: ```python list_open(user_id=None) ``` Dangerous. After: ```python list_open_for_user(user_id) list_open_internal() ``` Much better. This is the same pattern used in serious multi-tenant systems. --- # Remaining Issues Before Live ## 🔴 Issue 1 — Scheduler Reliability / Single Point of Failure This is the biggest remaining operational risk. Current: ``` FastAPI process | | asyncio scheduler | | Paper Position Manager ``` Question: What happens if: - server restarts? - process crashes? - deployment happens? - memory leak kills worker? Today: Position data survives. Good. But: Scheduler heartbeat/liveness is still important. I would add: ``` paper_scheduler_heartbeat table ``` Example: ``` id scheduler_name last_tick_at active_positions_count last_error server_id ``` Every 15 seconds: ``` update heartbeat ``` Admin dashboard: ``` Paper Trading Monitor Scheduler: 🟢 Running Last Tick: 10:24:15 Open Positions: 37 Last Error: None ``` Without this, you cannot confidently support customers. Priority: HIGH --- # 🔴 Issue 2 — Duplicate Scheduler Protection We discussed this earlier. Current safety: Single process. But production deployment can accidentally become: ``` Worker 1 | Scheduler Worker 2 | Scheduler ``` Then: ``` Position A Worker 1 closes Worker 2 closes Race condition ``` You added atomic close protection, which is good. But I still recommend: Add scheduler lease. Example: Database: ``` scheduler_locks name owner_id heartbeat expires_at ``` Only one instance owns: ``` paper_position_monitor ``` This protects you during: - scaling - deployment - accidental multi-worker configuration Priority: HIGH --- # 🟠 Issue 3 — Broker Session Expiry Handling Current architecture: Good: ``` Position | broker_connection_id | broker_session ``` But what happens: 9:15 User starts trading. Token expires: 13:00 Scheduler: ``` fetch LTP | | failed ``` Need explicit state: Example: ``` position_monitor_status: ACTIVE BROKER_SESSION_EXPIRED WAITING_FOR_RECONNECT ERROR ``` Do not silently continue. The UI should show: ``` ⚠ Position monitoring paused Reason: Broker session expired Reconnect broker ``` Priority: MEDIUM-HIGH --- # 🟠 Issue 4 — Paper Trading UI Real-Time Updates Current: Polling is acceptable. Example: ``` Browser | 15 sec polling | Database ``` For your first customers: This is OK. Later: Move to: ``` Scheduler | Event | WebSocket | UI ``` Not required before launch. Priority: MEDIUM --- # ✅ Issue 5 — Position Audit Trail Current: You have: ``` auto_paper_positions ``` But full lifecycle history? Need: ``` paper_position_events ``` Example: ``` POSITION_CREATED PRICE_UPDATED SL_MOVED TARGET_REACHED EXIT_TRIGGERED POSITION_CLOSED ``` Example: ``` NIFTY 24000 CE 10:24:15 Created 10:25:00 Price 120 10:31:20 Trailing SL moved 115 10:45:10 Target reached ``` This is critical for: - debugging - customer trust - research - AI improvement Priority: MEDIUM --- # 🔴 Issue 6 — Live Market Acceptance Test Before opening to users: I want one complete real market day test. Not code test. Real test: ## Test account: User A Broker connected. Morning: ``` 09:15 Start Shadow Mode ``` Wait for signal. Signal: ``` Shadow Signal Generated ``` Expected: ``` Paper Position Created ``` Close browser. Wait. Verify: ``` SL works Target works Trailing works Manual exit works ``` At end: Database: ``` Entry Exit P&L Reason Duration ``` This is mandatory. --- # My Final Production Readiness Score Current: ## Architecture: 9/10 Very good. ## Security: 9/10 Ownership problems fixed. ## Trading engine: 8.5/10 Logic is good. ## Operations: 7/10 Need monitoring + scheduler lease. ## Customer readiness: 7.5-8/10 --- # My recommendation I would NOT delay because of small things. I would do this: ## Phase 1 (Before first live customer) Must fix: ✅ Repository ownership intent ✅ Scheduler heartbeat ✅ Scheduler single-instance lock ✅ Broker expiry status ✅ One complete live market-day test Then: ``` Shadow Mode | Signal | Paper Trading | Background Position Manager | User UI ``` is production acceptable. --- The good news: **the difficult architectural mistakes are already fixed.** The remaining work is production hardening, not redesign. The biggest mistake would be adding more trading logic now. The system architecture is finally in the right direction; now focus on reliability, observability, and controlled rollout. 🟠 1. Scheduler heartbeat / crash detection 🟠 3. Broker token expiry during the same day Your test should include checking: Broker connected | | Position open | | Token still valid If token expires: Expected: Position remains open Monitoring status shows broker issue No wrong exit 🟠 4. Real signal quality validation This is not a software bug. But during your first live day check: When Shadow generates: BUY CE verify: Database: entry_price symbol strike option_type quantity SL Target matches the signal. This is the most important business validation. Urgent bugs I would still fix before public users Not before your personal test. These are production hardening items: | Item | Personal live test | Public launch | |-|-|-| | Scheduler heartbeat | Optional | Required | | Scheduler distributed lock | Optional (single laptop) | Required | | Broker expiry UI status | Monitor manually | Required | | Position event completeness | Existing logs OK | Verify | | Multi-user stress test | Not needed | Required | You are a senior backend architect and trading systems engineer. I need you to perform a deep code-level audit of the current Shadow Mode → Paper Trading integration. Do not assume anything. Do not say "this should work". Trace the actual implementation from the repository. My goal: When Shadow Mode generates a valid signal during live market hours: Shadow Mode | | Signal Generated | | Paper Trading Position Monitor | | Paper Position Created | | Background Position Manager | | SL / Target / Trailing / Exit Management I need to know exactly how this happens in the current system. ================================================== IMPORTANT ARCHITECTURE RULE ================================================== Shadow Mode must remain only a signal generator. Shadow Mode responsibilities: - Market observation - Strategy evaluation - Signal generation - Signal metadata creation Shadow Mode must NOT: - Manage positions - Monitor SL - Monitor Target - Execute exits - Update paper positions - Become the position manager Paper Trading Position Monitor responsibilities: - Receive valid signal - Create paper position - Own lifecycle after creation - Monitor live price - Manage SL - Manage Target - Manage trailing SL - Manage exit - Update database - Update UI ================================================== AUDIT QUESTIONS ================================================== 1. Find the exact location where Shadow Mode creates a valid signal. Provide: File: Function: Class: Line reference: Example: shadow_mode/capture.py run_shadow_analysis() creates ShadowDecision Explain: What object represents the signal? Example: ShadowDecision Signal object TradePlan Dictionary payload Database record Show the exact structure. ================================================== 2. After Shadow Mode creates a signal, what happens? Trace the complete call chain. I want: File A | Function A | calls | File B | Function B | calls | Paper Trading Do not provide a theoretical architecture. Show the real current path. ================================================== 3. Is there already an event/callback/queue mechanism? Check for: - Event bus - Queue - Database polling - API call - Background worker - Scheduler communication - Redis - RabbitMQ - Internal Python callback - Direct function call Answer: Current mechanism: OR No mechanism exists. ================================================== 4. If no handoff exists, where is the correct integration point? Recommend the smallest safe integration point. Possible examples: Option A: Shadow Mode writes a Signal Event: shadow_signals table | | Paper Trading consumer Option B: Shadow Mode calls: PaperExecutor.confirm() Option C: Internal event: ShadowSignalGeneratedEvent Explain which is safest and why. ================================================== 5. Check Paper Trading entry flow. Trace: Signal | TradePlan | PaperExecutor | auto_paper_positions Explain: What fields are required to create a paper position? Example: - user_id - broker_connection_id - symbol - trading_symbol - strike - option_type - entry_price - quantity - stop_loss - target - strategy_name - confidence ================================================== 6. Account ownership question. Very important. When Shadow Mode generates a signal: How does the system know: "This signal belongs to User A" Check: - scanner_sessions - user_id - broker_connection_id - active broker session - paper account Explain the ownership flow. Expected: Signal must contain: user_id or a reference that resolves to user_id A signal without ownership cannot create a customer paper position. ================================================== 7. Broker session question. When Paper Position Monitor manages a position: How does it know which broker connection to use? Trace: paper_position | | broker_connection_id | | broker_session | | isolated broker SDK Verify: - No global broker singleton - No user session switching - No cross-user broker access ================================================== 8. Background position monitoring. After the paper position is created: Does the existing background scheduler automatically detect it? Trace: Scheduler | | list_open_positions | | PositionManager | | Policy evaluation Confirm: - Position is adopted - User ownership preserved - Broker identity preserved ================================================== 9. Real-time UI update. After Shadow Mode creates a paper position: How does UI know? Trace: Database update | | API endpoint | | Polling/WebSocket | | Paper Trading UI Explain current behavior. ================================================== 10. Failure scenarios. Analyze: Case A: Shadow generates signal but Paper Trading creation fails. What happens? Case B: Paper position created but broker session expires. What happens? Case C: Duplicate signal generated twice. What prevents two paper positions? Case D: Two users have Shadow Mode running simultaneously. Can User A signal create User B position? ================================================== 11. Production recommendation. After audit, provide: Current status: READY or NOT READY If NOT READY: List exact missing components. Prioritize: P0 = must fix before live testing P1 = should fix before customers P2 = future improvement ================================================== 12. Final architecture diagram. Provide the actual current architecture: Example: User Login | Broker Session | Shadow Mode | Signal Object | ??? | Paper Executor | auto_paper_positions | Position Monitor Scheduler | Position Manager | Database | Paper Trading UI Highlight the missing "???" part if it does not exist. Important: Do not modify Shadow Mode. Do not redesign strategy logic. Do not change EMA logic. Do not change Paper Trading exit rules. Only analyze and recommend the safest signal handoff architecture. My final recommendation The Idea #2 is a good next step. But build it in this order: Phase 1 ✅ AI Auto Scan UI ✅ Start/Stop scan session ✅ User broker isolation ✅ Background scanner scheduler Phase 2 ✅ Signal detection ✅ Paper Admission Service ✅ Admission ledger ✅ PaperExecutor integration Phase 3 ✅ AutoPaper UI refresh ✅ Signal history ✅ Scan monitoring dashboard The existing Paper Position Monitor is already the correct foundation. Do not duplicate any position management logic inside AI Auto Scan. The clean separation should remain: AI Auto Scan = Find opportunities Paper Admission = Decide whether signal becomes a paper position Paper Position Monitor = Manage lifecycle until exit This keeps the system scalable and avoids repeating the ownership/broker isolation problems we already fixed. Yes. I have updated the prompt with the **3-phase implementation strategy** and included all decisions we finalized: - AI Auto Scan is separate from Index Scanner. - Shadow Mode remains untouched. - AI Auto Scan only generates signals. - Paper Admission controls Signal → Paper Position. - Paper Position Monitor owns the complete lifecycle. - One active scan per user is mandatory. - User broker isolation is mandatory. - No duplicate paper positions. - Production hardening included. Copy-paste prompt: ```text # AI Auto Scan Module — Production Implementation Plan ## Objective Build a new production module: AI Auto Scan The purpose of this module is to continuously analyze a user-selected market index using the logged-in user's broker session and automatically create Paper Trading positions when valid signals are detected. This feature must integrate with the existing Paper Trading Position Monitor architecture that is already implemented and fixed. IMPORTANT: Do not modify the existing Index Scanner page. Do not modify Shadow Mode core implementation. Do not create a new position management system. Do not duplicate Paper Trading monitoring logic. The architecture must follow: AI Auto Scan = Signal Generation Layer Paper Admission Service = Signal Acceptance + Position Creation Layer Paper Position Monitor = Position Lifecycle Management Layer ==================================================== FINAL ARCHITECTURE ==================================================== AI AUTO SCAN PAGE | | User selects Index | | Start AI Auto Scan | | AI Auto Scan Scheduler | User + Broker Context | | Signal Generation | | Signal Detected | | Paper Admission Service | | PaperExecutor.confirm() | | auto_paper_positions | | Paper Position Monitor | ----------------------- | | | P&L SL Target | Trailing SL | | Exit ==================================================== IMPLEMENTATION MUST BE DONE IN 3 PHASES ==================================================== # PHASE 1 # AI Auto Scan Infrastructure Goal: Create the complete AI Auto Scan foundation. No Paper Trading integration yet. ---------------------------------------------------- ## New Page Create: /ai-auto-scan Do not touch: /index-scanner Reuse the existing Index Scanner UI design language. Clone only the UI pattern: - Important Information card - Index selection cards - Dark theme styling - Layout structure Example: AI Auto Scan Select an Index [Nifty 50] [Bank Nifty] [Fin Nifty] [Nifty Midcap 50] [Sensex] ---------------------------------------------------- ## Index Selection Flow When user selects: NIFTY Display: NIFTY 50 AI AUTO SCAN Status: Stopped Broker: Connected Strategy: AI Signal Engine Button: START AI AUTO SCAN After starting: AI Auto Scan Running Index: NIFTY 50 Started: timestamp Status: Monitoring Market Last Analysis: timestamp Button: STOP AI AUTO SCAN ---------------------------------------------------- # Database Design Create: ai_auto_scan_sessions Required fields: id user_id index_symbol broker_connection_id status started_at stopped_at last_run_at stop_reason created_at updated_at Status examples: RUNNING STOPPED COMPLETED SIGNAL_GENERATED BROKER_DISCONNECTED MARKET_CLOSED ERROR ---------------------------------------------------- # Production Safety Requirement #1 ## One Active Scan Per User Mandatory rule: A user can have only ONE active AI Auto Scan session. Example: User starts: NIFTY Then tries: BANK NIFTY System must reject: "AI Auto Scan is already running for NIFTY. Stop the existing scan before starting another." Rules: - Validation must happen server-side. - Database is the source of truth. - Multiple browser tabs must not create multiple scans. - Multiple API requests must not create duplicate schedulers. - Background scheduler must enforce this rule. ---------------------------------------------------- # User Ownership Every scan belongs to: user_id Every scan must use: User ↓ User Broker Connection ↓ User Broker Session Never use: - shared broker session - global broker singleton - another user's token Reuse the existing broker isolation architecture already implemented in Paper Position Monitoring. ---------------------------------------------------- # Background Scheduler AI Auto Scan must run independently from browser. Browser only: - Start scan - Stop scan - View status Browser must NOT keep the scan alive. Scanner must continue when: - User closes browser - User refreshes page - User leaves website ---------------------------------------------------- # Market Rules Scheduler must validate: - Market open - Trading day - Broker session active Automatically stop: - Market close - Broker disconnected - User stopped manually - System failure ---------------------------------------------------- # PHASE 2 # Signal Generation + Paper Admission Goal: Connect: AI Auto Scan ↓ Signal ↓ Paper Position ---------------------------------------------------- # Signal Generation Reuse existing signal-generation logic. Do NOT modify: backend/shadow_mode/ Architecture: Shared Signal Engine | +----------------+ | | Shadow Mode AI Auto Scan Shadow Mode remains: Signal observation only. AI Auto Scan becomes: Signal generation consumer. ---------------------------------------------------- # Important TradePlan Rule Do not destroy the TradePlan. The flow must preserve: Signal Engine ↓ AnalysisResult ↓ TradePlan ↓ Paper Admission ↓ PaperExecutor.confirm() Do not convert the signal into a reduced object that loses: - quantity - contract details - entry plan - stop loss - target ---------------------------------------------------- # Signal Storage Create durable signal storage. Example: ai_auto_scan_signals Fields: id scan_session_id user_id symbol strategy trade_plan_json signal_time status created_at Purpose: - Recovery after restart - Audit - Debugging - Exactly once processing ---------------------------------------------------- # Paper Admission Service Create: paper_admission This layer is responsible for: - Validate user ownership - Validate broker connection - Validate strategy permission - Validate duplicate signal - Create paper position It must call: PaperExecutor.confirm() Do not bypass: PaperExecutor Do not directly insert: auto_paper_positions ---------------------------------------------------- # Admission Ledger Create: ai_scan_signal_admissions Fields: id signal_id user_id position_id status failure_reason created_at Unique constraint: (signal_id, user_id) Purpose: Prevent: - duplicate scheduler execution - retries creating duplicate positions - restart duplication ---------------------------------------------------- # Required Validation Before Creating Position Before PaperExecutor.confirm(): Validate: 1. User exists 2. User broker session exists 3. Broker session active 4. Strategy is allowed 5. Signal is not already admitted 6. Position does not already exist ---------------------------------------------------- # Failure Handling If signal is detected but position creation fails: Do not lose the event. Record: Signal detected ↓ Admission attempted ↓ Failed reason Examples: - broker unavailable - duplicate signal - strategy rejected - database error ---------------------------------------------------- # PHASE 3 # Auto Paper Production Experience Goal: Make the complete user experience production ready. ---------------------------------------------------- # Paper Position Ownership Once: auto_paper_positions is created: AI Auto Scan responsibility ends. Paper Position Monitor becomes the owner. It manages: - Live price - P&L - Stop Loss - Target - Trailing Stop Loss - Exit logic - Database updates Reuse existing system. Do not create another monitor. Not in use for now ---------------------------------------------------- # AutoPaper UI Current UI assumes manual confirmation. Update it for automatic positions. Requirement: If: AI Auto Scan creates a position The user should see it automatically. Do not depend on: openCount > 0 The UI should continue checking server state during market hours even when there are currently zero open positions. ---------------------------------------------------- # Signal Audit Trail Maintain complete lifecycle: Signal Generated ↓ Admission Attempted ↓ Paper Position Created ↓ Position Managed ↓ Exit Use existing audit/log architecture where possible. ---------------------------------------------------- # Production Hardening Checklist Before production testing: ## Safety [ ] One active scan per user [ ] User broker isolation [ ] No shared broker session [ ] No duplicate positions [ ] Admission ledger implemented [ ] Signal recovery after restart ## Architecture [ ] Shadow Mode untouched [ ] Index Scanner untouched [ ] Paper Position Monitor unchanged [ ] No duplicate position manager created ## Reliability [ ] Market close automatically stops scanner [ ] Broker disconnect handled [ ] Failed admissions recorded [ ] Background scheduler restart safe ---------------------------------------------------- # Final Rules AI Auto Scan: Find signals. Paper Admission: Decide whether signal becomes paper trade. Paper Position Monitor: Manage the position until exit. Never combine these responsibilities. The final production flow: User Login ↓ Connect Broker ↓ AI Auto Scan ↓ Signal Found ↓ Paper Admission ↓ Paper Position Created ↓ Paper Position Monitor ↓ SL / Target / Trailing / Exit This implementation must preserve the existing ownership model, broker isolation model, and Paper Trading Position Monitor architecture. ``` This version is safer because the coding agent cannot accidentally build everything as one giant feature. It forces the implementation order: **Phase 1 → prove scanner infrastructure** **Phase 2 → prove signal admission** **Phase 3 → production user experience**. Here are the four questions I asked and the answers you gave: 1. Billing — how should scan credits be charged? ✅ Charge on admission (your pick) — one credit per signal that actually becomes a paper position. Cycles that find nothing are free. Needs an SL-01 amendment naming AUTO_SCAN as a second billing surface. Charge per confirmable cycle — is_billable_analysis() unchanged on every cycle. One credit at session start. No billing at all. 2. Cadence — how often to analyse, and does it keep going after a position? Timeframe cadence, keeps running. ✅ Timeframe cadence, stops after first position (your pick) — analyse once per candle close of the selected timeframe (1m→60s, 5m→300s, 15m→900s); session moves to SIGNAL_GENERATED and stops once one paper position is created. One trade per session. Fixed 60s cadence, keeps running. 3. Scan config — what does the user choose besides the index? ✅ Index only (your pick) — strategy auto, timeframe and risk-reward from the administrator's defaults via GET /api/v2/capabilities. Matches your page mock ("Strategy: AI Signal Engine"). Index + strategy. Index + strategy + timeframe + risk-reward. 4. Position cap — should admission cap positions per session? One open auto-scan position at a time. ✅ No extra cap (your pick) — rely on PaperExecutor's existing duplicate rule. (Effectively moot given #2: the session stops after its first position.) Continuing with the research. 23 Aug 2026: The architecture I recommend AI AUTO SCAN │ Select Index │ ▼ Production Strategies filtered by validated_markets │ ▼ Select Strategy │ ┌──────────┴──────────┐ ▼ ▼ validated_timeframes validated_rr │ │ ▼ ▼ Select TF Select R:R │ │ └──────────┬──────────┘ ▼ Validate SAME Registry Row │ ▼ Start Scan │ ▼ Frozen Scan Session strategy + timeframe + R:R + index │ ▼ AI Analysis │ ▼ Signal │ ▼ Auto Admission │ ▼ Paper Position │ ▼ Position Monitor The strongest architectural result here is that all three surfaces can eventually converge on the same production strategy path without duplicating strategy logic: strategy_registry │ production_strategy_service │ ┌─────────────┼─────────────┐ ↓ ↓ ↓ AI Auto Scan Index Scanner Market Analysis │ │ │ └─────────────┼─────────────┘ ↓ run_analysis() ↓ get_production_signal_engine() ↓ Production Strategy ↓ TradePlan 24 Aug 2026: Final Architecture: Signal Engine | | assemble_entry_plan_for_options | frame.instrument | ------------------------------- | | | | index_option stock_option | | | | option_selector.py stock_option_selector.py | | | | NIFTY/BANKNIFTY RELIANCE/TCS/INFY