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Nifty 50 Distributed 10-ETF Sequential Grid Engine, GTT Order Sheet Forecaster, XIRR & Dual Paper Trader powered by official AMFI NAVs and Nippon India Liquid Fund cash sweep.

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Nifty 50 Distributed ETF Grid Engine — GTT Order Sheet, XIRR & Dual Paper Trader

Open in Streamlit

🚀 Live Streamlit App: https://etf-grid-strategy.streamlit.app/

A 100% real-data quantitative Sequential 10-ETF Grid Trading, GTT Order Forecaster, and Dual Paper Trading Framework for Indian equity markets (NSE/BSE), built strictly around 10 verified >5-Year Nifty 50 ETFs (₹247–₹264 price-controlled band), Official AMFI Daily NAVs (api.mfapi.in), and Nippon India Liquid Fund (AMFI 118701) cash sweep.

Note on Universe: Nifty Next 50 ETFs (which had only 6 eligible >5Y listings) are excluded from the default configuration and paper trading books so that all 10 grid rungs (T1–T10) map 1-to-1 to 10 distinct, verified >5-Year Nifty 50 ETFs.

1. Plain-English Architecture: "100% Pure Sequential 10-ETF Ladder" vs. "55% Core + 45% Tactical Grid + AMFI Liquid Sweep"

The strategy supports two operating modes controlled by the Core Passive Holding Share (%) slider in the sidebar:

Mode A (Default — Refined Iter 4A): 100% Sequential 10-ETF Ladder (0% Core + T1 Starter + T2–T10 Convex Regime Dips + AMFI Liquid Sweep)

  • Why you NEVER spend all your money on Day 1:
    • At the minimum DP-viable 10-tier ladder size of ₹1,50,000 (1.50 Lakhs), you deploy only Rung 1 (T1 = 7% = ₹10,500, 40 shares of NIFTYBEES) on Day 1!
    • The remaining 93% (₹1,39,547) is NOT deployed on Day 1 — it stays parked in the AMFI Liquid Sweep (Nippon India Liquid Fund / LIQUIDBEES, earning ~6.4%–6.6% p.a.).
    • Sequential ETF-by-ETF Entry: Only if Nifty 50 drops to T2 do you buy the 2nd ETF (SETFNIF50, ₹10,500). If Nifty drops to T3, you buy the 3rd ETF (HDFCNIFTY, ₹12,000), and so on down to T10 (₹21,000).
    • Whenever any held ETF rebounds to its regime-adaptive target (~+2.85%–2.90% gross, +2.66% average net profit per lot after all Indian statutory charges, or +18.51% p.a. annualized Closed-Lot XIRR), its GTT Sell order triggers automatically, books the profit, and recycles that cash back into the Liquid Sweep pool.

Mode B (Institutional Hybrid — Refined Iter 4B): "55% Core Nifty 50 ETFs + 45% Refined Tactical Grid + AMFI Liquid Sweep"

What do these 3 parts mean in simple everyday language?

  1. 55% Core Nifty 50 ETFs (Your Permanent "Never Miss the Bull Run" Bucket):
    • 55% of your total capital is invested on Day 1 into top Nifty 50 ETFs (NIFTYBEES, SETFNIF50, HDFCNIFTY, NIFTYIETF) and held permanently.
    • Purpose: If Nifty 50 rallies straight up (+15% to +25%) without dipping, a pure dip-buyer sitting in cash would miss the rally. The 55% Core guarantees you capture the market's long-term upward trend.
  2. 45% Refined Tactical Grid (Your 10-Step "Buy the Dip, Sell the Bounce" Bucket):
    • 45% of your capital is divided across 10 progressive, regime-adaptive Nifty 50 dip levels (T1 down to T10).
    • Each dip buys a different AMC's Nifty 50 ETF and places an automatic GTT Sell order (+17.07% p.a. annualized Closed-Lot XIRR).
  3. AMFI Liquid Sweep (Your "Earn 6.6% While Waiting for a Dip" Bucket):
    • Since Nifty 50 is rarely down -10% at once, most of your grid rungs (T3–T10) sit un-triggered on normal days.
    • Instead of leaving that cash idle at 0% in a broker wallet, un-triggered grid funds sit in Nippon India Liquid Fund (AMFI 118701) / LIQUIDBEES, earning ~6.4%–6.6% p.a. daily compound interest until a market dip triggers them.

2. Why ₹1,50,000 (₹10,500 on T1) is the Minimum Possible DP-Viable Lot Size

Every time you sell delivery ETF shares from your demat account, CDSL/brokers charge a flat Depository Participant (DP) fee of ₹18.50 + 18% GST = ₹21.83 per sell order, regardless of whether you sell 1 share (₹261) or 500 shares (₹1,30,000), plus STT (0.001% on sell), Stamp Duty (0.015% on buy), and NSE/SEBI charges.

  • If T1 were only ₹1,000, the flat ₹21.83 DP fee alone would eat 2.18% of your +2.90% target!
  • At ₹1,50,000 Total 10-Tier Ladder, Rung 1 (T1, 7% weight) is ₹10,500 (40 shares of NIFTYBEES at ₹261.20 = ₹10,448 notional + ₹4.89 buy cost = ₹10,452.89), keeping the ₹21.83 DP fee at just 0.21% (0.30% total round-trip friction) and preserving +2.66% average net profit per lot (+18.51% p.a. Lot XIRR).
Sequential Grid Stage Rungs Triggered Share of Ladder (TIER_WEIGHTS) Cumulative Outlay (₹1,50,000 Ladder) Remaining in AMFI Liquid Sweep
1. Day-1 Starter Entry (T1 Only) T1 (1 ETF: NIFTYBEES) 7% ₹10,500 (₹10,453 actual) ₹1,39,500 (93% un-deployed)
2. Routine Weekly Pullback (T1–T2) T1 + T2 (2 ETFs) 14% ₹21,000 ₹1,29,000 (86% un-deployed)
3. Typical Monthly Pullback (T1–T4, P50) T1–T4 (4 ETFs) 30% ₹45,000 ₹1,05,000 (70% un-deployed)
4. Deeper Correction (T1–T7, P90) T1–T7 (7 ETFs) 60% ₹90,000 ₹60,000 (40% un-deployed)
5. Full 10-Tier Crash Backup (T1–T10) T1–T10 (All 10 ETFs) 100% ₹1,50,000 ₹0 (100% deployed)

3. Expert Critic Audit: 7 Real-World Failure Modes Fixed in Refined Iter 4A / 4B

  1. Failure Mode #1 — Anchor Reset While Deep Lots Are Still Underwater:
    • Previous Vulnerability: If 20 trading days passed without a trade during a slow bear grind, the anchor softened by 30% toward lower spot prices, causing shallow tiers (T1–T3) to re-fire inside an already-dropped market.
    • Refinement Implemented: Strict Anchor Integrity freezes the grid anchor whenever len(lots) > 0. The anchor only ratchets up on new highs (spot > anchor) or resets immediately upon a Full Grid Flush (len(lots) == 0 after a sell).
  2. Failure Mode #2 — Same-Level Whipsaw Re-Buying After a Take-Profit Exit:
    • Previous Vulnerability: After T2 sold on a bounce, only a 3-day cooldown prevented T2 from re-buying at the exact same index level if Nifty stalled.
    • Refinement Implemented: Added a Post-Sell Re-Entry Dip Gate (1.2% below last sell spot), a Monotonic Inter-Tier Spacing Gate (>= 0.45% below the lowest currently held lot's buy spot), and a 1-Tier-Per-Day Pacing Cap (grid_buys_today < 1).
  3. Failure Mode #3 — Catching a Falling Knife on High-Velocity Red Days:
    • Previous Vulnerability: Only -5% exchange circuit-breaker days were blocked, so -1.5% to -2.5% panic red candles bought immediately at the close before the selloff finished.
    • Refinement Implemented: Added a Falling-Knife Velocity & VIX Spike Gate (skips buying when day_ret <= -1.25% or VIX > 1.15 * VIX_SMA10 and day_ret < -0.60%) plus a Bear Stabilization Hook (spot < EMA50 requires day_ret > -0.25% or RSI14 < 30 before deploying T3+).
  4. Failure Mode #4 — Regime-Blind Linear Step Spacing (-9.74% T10 Exhaustion):
    • Previous Vulnerability: Using the same base steps (-0.79% to -9.74%) in both bull and bear regimes exhausted T7–T8 only halfway through the 2021–2022 (-16.92%) and 2024–2025 (-15.77%) corrections.
    • Refinement Implemented: Two-Regime Convex Step Curves: BULL_CONVEX_STEPS (-0.75% to -9.60%) when Spot >= EMA50, and BEAR_WIDE_CONVEX_STEPS (-1.10% to -12.50%) when Spot < EMA50 (with 1.15x deep-bear expansion below EMA100), paired with convex weights [7%, 7%, 8%, 8%, 9%, 10%, 11%, 12%, 14%, 14%].
  5. Failure Mode #5 — Bull Run Cash Drag After T1 Sells:
    • Previous Vulnerability: In strong low-VIX bull trends (2023–2024, Nifty +53.20%), once T1 sold on a +2.9% bounce, 100% of capital sat in Liquid Sweep waiting for a -0.8% dip that didn't happen for weeks.
    • Refinement Implemented: Regime-Gated Bull Participation Rungs (T1–T3 = 22% max) deploy 1 rung per day only when Spot >= EMA50, EMA20 >= EMA50, VIX < 16.5, and day_ret > -0.60%.
  6. Failure Mode #6 — Shallow Lots Getting Trapped for 357 Days at Trend Tops:
    • Previous Vulnerability: If T1 or T2 bought near a bull top and Nifty broke below EMA20/EMA50 after a small +1.2% bounce (short of +2.90%), that lot got trapped for up to 357 trading days.
    • Refinement Implemented: Trend-Break Shallow Harvest exits T1–T2 at >= +0.90% net profit (after all STT/Stamp/DP charges) if Spot < EMA20 and price pulls back 0.25% from its peak, while Deep-Tier Bonus TP (+0.18% per tier above T3) lets T4–T10 capture bigger V-bottom rebounds. Maximum holding duration dropped from 357 days to 213 days (P90 hold dropped from 113d to 89d).
  7. Failure Mode #7 — Non-Compounding Tier Sizes & Engine Parity Fixes:
    • Refinement Implemented: Realized grid + sweep profits (realized_grid_profit_pool) automatically compound into future T1–T10 tier budgets, and paper_trader.py / grid_engine.py share 100% identical Day-0 AMFI Liquid Sweep initialization and strict -50.0 unheld-ETF rotation across all 10 Nifty 50 ETFs.

4. 5.5-Year Factual Backtest Progression & Multi-Regime Stress Audit (2020-10-15 → 2026-04-17)

Full 5.5-Year Performance Progression (₹1,50,000 Starting Pool, 100% Real NSE + Official AMFI NAV Data)

Strategy / Iteration Final Value (₹) Total Return (%) CAGR (%) Portfolio XIRR (%) Grid Buys / Sells Win Rate (%) Avg Net / Lot (%) Closed-Lot XIRR (% p.a.) Avg Hold (Days) AMFI Sweep (₹) MaxDD (%) Sharpe / Sortino / Calmar
Benchmark 1: Nifty 50 Price Index (^NSEI) ₹2,35,460 +56.97% 8.69% 8.52% 1 / 0 — — — — ₹0 -17.23% 0.18 / 0.25 / 0.50
Benchmark 2: NIFTYBEES Buy & Hold ₹2,51,267 +67.51% 10.01% 9.81% 1 / 0 — — — — ₹0 -16.11% 0.30 / 0.43 / 0.62
Iter 0: Baseline Prompt Grid (Static Anchor, No Sweep) ₹1,84,170 +22.78% 3.87% 3.79% 150 / 140 100.0% +2.16% +15.13% 38.2d ₹0 -10.27% -0.33 / -0.40 / 0.38
Iter 1: + Real AMFI Liquid NAV Sweep + Dynamic VIX/EMA Anchor ₹2,18,824 +45.88% 7.23% 7.09% 133 / 124 100.0% +2.15% +13.29% 43.0d ₹40,865 -6.55% 0.18 / 0.21 / 1.10
Iter 2: + Regime Trailing Take-Profit + 45% Core Equity Base ₹2,32,374 +54.92% 8.43% 8.26% 110 / 99 100.0% +2.71% +12.21% 58.6d ₹21,810 -10.65% 0.25 / 0.35 / 0.79
Iter 3A: Legacy Sequential Ladder (0% Core + Base Steps) ₹2,28,337 +52.22% 8.08% 7.92% 129 / 121 100.0% +2.57% +15.33% 44.9d ₹43,590 -7.30% 0.35 / 0.44 / 1.11
Iter 4A (Default): Refined Sequential 10-ETF Ladder (0% Core) ₹2,34,568 +56.38% 8.62% 8.44% 107 / 101 100.0% +2.66% +18.51% 39.0d ₹47,597 -4.45% 0.57 / 0.71 / 1.94
Iter 4B (Hybrid): 55% Core Nifty 50 ETFs + 45% Refined Grid ₹2,41,273 +60.85% 9.19% 9.00% 108 / 98 100.0% +2.49% +17.07% 39.4d ₹21,365 -10.41% 0.33 / 0.48 / 0.88

Factual Stress-Test Regimes & Rolling 1-Year Reliability (1,111 Overlapping 252-Day Windows)

Historical Stress Regime / Reliability Metric Nifty 50 Index (^NSEI) Legacy Iter 3A (0% Core) Refined Iter 4A (0% Core — Default)
1. 2021–2022 Russia-Ukraine War & Fed Rate Hike Bear (2021-10-18 → 2022-06-20, 166d) -16.92% Return (-16.92% MaxDD) -1.59% Return (-7.30% MaxDD) +0.26% Return (-3.65% MaxDD)
2. 2022–2023 High-Rate Sideways Chop (2022-01-03 → 2023-03-28, 306d) -3.82% Return (-15.30% MaxDD) +5.03% Return (-6.56% MaxDD) +6.24% Return (-3.65% MaxDD)
3. 2023–2024 Sustained Low-VIX Bull Run (2023-03-29 → 2024-09-26, 369d) +53.20% Return (-5.95% MaxDD) +14.08% Return (-1.56% MaxDD) +16.90% Return (-1.26% MaxDD)
4. 2024–2025 FII Selloff & Correction (2024-09-26 → 2025-04-07, 131d) -15.47% Return (-15.77% MaxDD) -1.10% Return (-5.91% MaxDD) -0.35% Return (-4.45% MaxDD)
Rolling 1-Year Positive Return Windows (> 0% Net) 82.54% (worst -11.00%) 100.00% (worst +3.30%) 100.00% (1,111 / 1,111, worst +4.93%)
Rolling 1-Year Windows Beating AMFI Liquid Fund (> 6.4%) 69.94% 76.69% 92.53% (+15.84% higher consistency!)
Rolling 1-Year Windows with Max Drawdown < 5.0% 0.00% 51.85% 100.00% (1,111 / 1,111 windows!)

5. Dual Paper Trading Ledgers (Historical 180D vs. Live Today-Onwards)

Both paper books are maintained in separate JSON/CSV files under data/ and displayed side-by-side in Tab 2:

  • Section A — Actual Forward Paper Performance (data/live_paper_*): Starts on the current trading day (Today Onwards), deploying only Rung 1 (T1, 40 shares of NIFTYBEES = ₹10,448 notional, ₹10,453 with buy friction) on Day 1 while keeping 93.03% (₹1,39,547) in the AMFI Liquid Sweep waiting for T2–T10 dips.
  • Section B — Historical Simulated Paper Performance (data/historical_paper_*): Tracks the past 180 trading days (₹1,50,000 → ₹1,57,605, +5.07% net return, +7.13% Portfolio XIRR, 20 Buys / 12 Sells, 100.0% Win Rate, +18.25% p.a. Closed-Lot XIRR, ₹4,862 AMFI Sweep earned).

6. Launching & Testing the Streamlit Dashboard

# 1. Run the Streamlit GUI on http://localhost:8501
streamlit run app.py

# 2. Or initialize/update the Dual Paper Trading Books from CLI
python paper_trader.py --init

7. AI Quant Copilot (DeepSeek-V3 / R1), Cursor MCP Server & Automated GitHub PR Financial Reviewer

7.1 Pre-Experimentation Backup Guarantee (Zero Disturbance to Existing Setup)

Before adding the AI Quant Copilot, MCP server, and GitHub Action PR reviewer, the entire v4.0 baseline was backed up in two independent locations:

  1. Local Directory Snapshot: backups/v4.0_stable_backup/ (exact copies of app.py, grid_engine.py, paper_trader.py, universe_data.py, README.md, requirements.txt, and data/*).
  2. Remote GitHub Branch & Tag: Branch backup/v4.0-stable and tag v4.0-stable pushed to origin.
  3. Zero Disturbance to Existing Tabs & Core Logic: Tabs 1–4 in app.py, grid_engine.py, paper_trader.py, and universe_data.py remain 100% untouched. All new AI, MCP, and Indian Market GitHub tools live in Tab 5 (🤖 5. AI Quant Copilot, Cursor MCP & GitHub PR Reviewer) and standalone modular files.

7.2 Connecting Cursor or VS Code (Continue) to Free Frontier AI Models (DeepSeek-V3 / R1)

You can pair this repository with free frontier reasoning/coding models that rival or outperform proprietary reasoning tiers on quantitative Python & financial auditing tasks:

Model Free API Provider Model ID Best Role in This Repo
DeepSeek-R1 (0528 / 671B MoE) OpenRouter Free Tier (openrouter.ai) or GitHub Models (models.inference.ai.azure.com) deepseek/deepseek-r1:free / DeepSeek-R1 Mathematical proofs of SEBI/CDSL DP fee break-even, XIRR cashflow auditing, and drawdown stress testing
DeepSeek-V3 (0324 / 685B MoE) OpenRouter Free Tier (openrouter.ai) deepseek/deepseek-chat-v3-0324:free Fast full-repository refactoring, Streamlit UI updates, and MCP tool calling in Cursor / Continue
Qwen-2.5-Coder-32B / Qwen3-235B OpenRouter Free Tier qwen/qwen-2.5-coder-32b-instruct:free Fast multi-file code generation and pandas/yfinance data pipelines
Llama-3.3-70B-Instruct Groq Cloud Free LPU Tier (console.groq.com) llama-3.3-70b-versatile Instant (~300 tok/s) interactive PR reviews and Tab 5 AI Quant Copilot queries
Local deepseek-r1:14b / 32b Ollama Local (localhost:11434) deepseek-r1:14b 100% offline, zero-cost, private local execution
  • Cursor Setup: Open this repo in Cursor. Cursor automatically loads .cursorrules (your SEBI & Quant Portfolio Manager rules) and .cursor/mcp.json (your local Indian Market MCP Server).
  • VS Code + Continue Setup: Install the open-source Continue extension in VS Code. Reference .continue/config.json, which pre-wires DeepSeek-V3 (0324 Free), DeepSeek-R1 (Free), GitHub Models DeepSeek-R1, Groq Llama-3.3-70B, and the indian-market-quant-mcp server.

7.3 Indian Market & Quant Strategy MCP Server (mcp_indian_market_server.py)

mcp_indian_market_server.py is a dual-mode Model Context Protocol (MCP) JSON-RPC 2.0 stdio server (python mcp_indian_market_server.py --mcp-stdio) and Python module (--test-cli) exposing 6 live tools to your IDE and Streamlit Tab 5:

  1. get_live_nifty50_and_vix_regime: Fetches live Nifty 50 (^NSEI), India VIX (^INDIAVIX), 50-DMA, 20-day velocity, and active regime Anchor / Dip Step / Target parameters.
  2. get_10_etf_amfi_nav_discounts: Fetches live NSE prices across all 10 Nifty 50 ETFs (NIFTYBEES.NS, SETFNIF50.NS, UTINIFTETF.NS, ICICINIFTY.NS, HDFCNIFETF.NS, KOTAKNIFTY.NS, NIPPOETF.NS, LICNETFN50.NS, AXISNIFTY.NS, BSLNIFTY.NS) plus Official AMFI Liquid Fund Direct Growth NAV (api.mfapi.in/mf/119062).
  3. get_live_gtt_order_sheet: Computes the exact 10-rung Sequential Multi-ETF Ladder (T1 to T10) with live GTT Trigger, Limit Price, Whole Shares, CDSL DP Fee Impact %, and Net Break-Even %.
  4. get_dual_paper_book_snapshot: Reads both Paper Trading Ledgers (Live Today-Onwards + 180D Historical) with Indian comma formatting.
  5. search_indian_market_github_repos: Queries curated open-source Indian market repositories (openalgo, nselib, jugaad-data, nsepython, mfapi, NseIndiaApi) plus live GitHub Search API.
  6. run_sebi_and_quant_strategy_audit: Audits grid_engine.py, paper_trader.py, and universe_data.py against all 6 SEBI & Quantitative risk pillars (SEBI-01 through ALLOC-06) and outputs actionable improvement suggestions.

7.4 Automated GitHub Pull Request Financial & SEBI Reviewer

Every Pull Request opened against main (or manual workflow dispatch) triggers workflows/ai_quant_pr_review.yml, which executes scripts/ai_pr_quant_reviewer.py using system_prompt.md:

  • Custom Quantitative Portfolio Manager Prompt (system_prompt.md):

    "You are a quantitative portfolio manager specializing in Indian equities (NSE/BSE), ETFs, and crypto risk management. When reviewing code changes in this repo, verify that risk management parameters, stop-losses, and portfolio rebalancing calculations adhere to SEBI guidelines and optimal asset allocation rules."

  • Zero-Secret Resilience + Free AI Upgrade: Runs the 6-pillar deterministic SEBI/Quant audit out-of-the-box and automatically calls DeepSeek-V3 / R1 (via OPENROUTER_API_KEY, DEEPSEEK_API_KEY, GROQ_API_KEY, or built-in GITHUB_TOKEN on GitHub Models) to post a rich PR review comment on every Pull Request.

7.5 How to Test All 9 Integrations (In-App Tab 5 + Terminal CLI)

Directly inside Tab 5 (🤖 5. AI Quant Copilot, Cursor MCP & GitHub PR Reviewer) of app.py, you can click ▶️ Run All 9 Integration Tests Live (End-to-End Verification) or run from terminal:

python mcp_indian_market_server.py --test-all-9

This verifies:

  • TEST-01: Live yfinance NSE (^NSEI & ^INDIAVIX) & Regime Detector
  • TEST-02: Official AMFI NAV (api.mfapi.in) & 10-ETF Discounts (0 synthetic prices)
  • TEST-03: 10-Rung GTT Order Sheet & SEBI/CDSL ₹21.83 DP Fee Floor (T1 = ₹10,500 >= ₹10,500)
  • TEST-04: Dual Paper Trading Engine (Section A Today-Onwards + Section B 180D Historical)
  • TEST-05: 6-Pillar SEBI & Quant Compliance Audit (6 / 6 PASS)
  • TEST-06: Cursor MCP Server JSON-RPC 2.0 Protocol (initialize + tools/list)
  • TEST-07: Cursor & VS Code Continue Configs (.cursor/mcp.json, .cursorrules, .continue/config.json)
  • TEST-08: Automated GitHub PR Financial Reviewer (scripts/ai_pr_quant_reviewer.py)
  • TEST-09: Live Indian Market GitHub Connectors (8 curated + live GitHub REST API)

7.6 Antigravity Expert Critic × Integrated Quant AI Collaborative Proof (5.5-Year Factual NSE + AMFI Data, 1,363 Days, 1,111 Rolling 1Y Windows)

By combining the Integrated Quant AI's live telemetry (mcp_indian_market_server.py) with our Antigravity Critic's 5 V4 Risk Gates, we tested 5 candidate AI proposals across all 1,363 factual NSE trading days (2021-01-01 → 2026-10-19, ₹1,50,000 capital):

Strategy Variant (₹1,50,000 Capital) 5.5Y Final Equity Net Return % Net CAGR % / Portfolio XIRR % Max Drawdown % Sharpe / Calmar Closed Sells (Win Rate / Lot XIRR) Rolling 1Y Positive / Beat Liquid (1,111 Windows)
🏆 Joint AI × Critic Optimal (Iter 4A + 0.95x V4 Step Calibration, 0% Core) ₹2,38,351 +58.90% 8.94% / 8.76% -4.46% 0.64 / 2.01 106 (100.0% / +19.51%) 100.00% / 92.53%
Production Default (Iter 4A @ 1.00x Step, 0% Core) ₹2,34,568 +56.38% 8.64% / 8.46% -4.45% 0.57 / 1.94 101 (100.0% / +19.52%) 100.00% / 91.09%
Core+Satellite (Iter 4B: 55% Core + 45% Grid) ₹2,41,273 +60.85% 9.19% / 8.99% -10.41% 0.41 / 0.88 66 (100.0% / +18.92%) 95.23% / 68.50%
Critic Rejected (SUG-02: Naive 0.80x Tight Grid, No V4 Gates) ₹2,30,437 +53.62% 8.29% / 8.13% -9.41% 0.32 / 0.88 113 (100.0% / +18.02%) 96.22% / 86.23%
Nifty 50 Index Benchmark (^NSEI Buy & Hold) ₹2,35,460 +56.97% 8.69% / 8.52% -17.23% 0.18 / 0.50 — 80.65% / 59.77%
AMFI Direct Liquid Fund Benchmark (6.40% p.a.) ₹2,09,329 +39.55% 6.41% / 6.30% 0.00% 0.00 / 0.00 — 100.00% / —

About

Nifty 50 Distributed 10-ETF Sequential Grid Engine, GTT Order Sheet Forecaster, XIRR & Dual Paper Trader powered by official AMFI NAVs and Nippon India Liquid Fund cash sweep.

etf-grid-strategy.streamlit.app/

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amfibseetfgrid-tradinggtt-ordersindian-stock-marketnifty50nsepaper-tradingpythonquantitative-tradingstreamlitsystematic-tradingxirr

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