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BankNifty Strategy Engine — README

A resumable, LLM-driven intraday engine that digests sentiment, expert transcripts, technicals (RSI/MACD), and news to produce trade plans for BankNifty (and Nifty for context). The engine simulates executions on 1-minute data, evaluates P&L, and keeps airtight checkpoints so you can resume exactly where you left off after interruptions.

Features
Project structure
Data inputs & expected columns
Installation
Configuration (.env + config classes)
Running
Resumable checkpoints
How the engine works (timeline)
Trade simulation rules
LLM JSON schemas
Outputs

Features

🧠 LLM-assisted trade plans using structured JSON outputs (strict schema).
📰 News-aware decisions (hourly, last 15 mins at close).
📈 Technicals: RSI + MACD on hourly/daily series.
🧪 1-minute backtest execution with deterministic tiebreak rules.
🔁 Resumable runs via checkpoint (safe to kill & rerun).
✅ Flip/No-trade exit enforcement: if plan flips side or says “No trade” while holding, engine exits at market price.
🧠 Memory string summarizing the last completed trade gets fed back into prompts.
📦 Excel & Parquet logs for analysis.

Project structure

banknifty_strategy/
├─ app/
│  ├─ __init__.py
│  ├─ engine.py              # Core loop (09:15 → … → 15:30)
│  ├─ models.py              # Pydantic v2 models
│  ├─ prompts.py             # Prompt templates: morning / intrahour / closing
│  ├─ simulator.py           # simulate_trade_from_signal, slice_intraday
│  ├─ dataio.py              # load_data() – reads/normalizes data frames
│  ├─ checkpoint.py          # CheckpointManager – resume & append parquet logs
│  ├─ logging_setup.py       # Rotating file/logger
│  ├─ config.py              # AppConfig, Paths, LLMConfig
│  ├─ llm.py                 # OpenAI client wrapper; strict JSON schema handling
│  ├─ news.py                # summaries_between() helpers
│  ├─ utils.py               # hour_passed(), hourly_ohlc_dict(), helpers
│  └─ writer.py              # to_excel_safely()
├─ scripts/
│  └─ run_backtest.py        # CLI entrypoint
├─ requirements.txt
└─ README.md

Note: Keep app/ a proper package (it must include __init__.py). Always run from the project root so imports like from app.engine import Engine work.

Data inputs & expected columns

Your loader (app/dataio.py) should read and normalize sources. The engine expects these canonical frames and columns:

1. BankNifty hourly (`df_bn_hourly`)

Columns: datetime, open, high, low, close, RSI, MACD_Line, Signal_Line
Granularity: hourly (09:15, 10:15, …, 15:15, 15:30)
Used for: 09:15 previous indicators, hourly OHLC dicts, close price lookup.

2. BankNifty 1-minute (`df_bn_1m`)

Columns: datetime, open, high, low, close
Granularity: 1 min
Used by: simulate_trade_from_signal execution windows.

3. Nifty daily or daily-like context (`df_nifty_daily`)

Columns: datetime, open, high, low, close, RSI, MACD_Line, Signal_Line
Used for: contextual morning prompt.

4. Sentiment predictions (`df_sentiment`)

Columns: predicted_for (datetime), proposed_sentiment, reasoning

5. Expert transcript (`df_transcript`)

Columns: prediction_for (datetime), Transcript
- (If your raw file has Prediction_for_date, normalize to prediction_for.)

6. News with summaries (`df_news`)

Columns: datetime_ist (datetime), Article_summary (string)

Ensure all datetime columns are timezone-normalized (naive or same tz) and parsed.

Installation

Python 3.9+ recommended.

pip install -r requirements.txt

Configuration (.env + config classes)

Create a .env in project root:

OPENAI_API_KEY=EMPTY
OPENAI_BASE_URL=http://localhost:8000/v1
OPENAI_MODEL=Qwen/Qwen3-4B

Edit temperature and top_p values from:

app/config.py

Running

Use the provided script. Minimal edits added --ckpt-dir (defaults to <out-dir>/checkpoint).

# Always run from project root so "app" package is on sys.path
python -m scripts.run_backtest   --data-dir ./data   --out-dir ./result   --start "2023-12-29 15:15"   --end   "2024-05-01 09:15"

Resumable checkpoints

The engine persists state and logs in Parquet inside --ckpt-dir:

<ckpt-dir>/
├─ checkpoint.json           # last_timestamp_processed, state, plans, memory_str
├─ trade_log.parquet
├─ stats_log.parquet
├─ expert_log.parquet
└─ summary_log.parquet

You can kill the process and re-run with the same --ckpt-dir. The engine:

Reads checkpoint.json
Skips timestamps already processed
Continues from the next tick

Excel mirrors (*.xlsx) are written to --out-dir for human inspection.

How the engine works (timeline)

At each timestamp ts in your hourly series:

09:15 — Morning
- Gathers Nifty/BankNifty previous OHLC + indicators.
- Pulls sentiment + expert transcript.
- Calls LLM (schema SummaryMorning) → morning summary.
- Calls LLM (schema TradePlan) → first plan of the day (but the actual open/close is derived dynamically from previous state; first day has no memory).
10:15 — First intrahour
- Simulate 1-minute window from last slice start → 10:15 using current plan.
- Log state change only if it changed by value (not identity).
- If exited naturally (stop/target), update memory_str, reset state, move last_slice_start.
- Pull last hour news, OHLC dict, current indicators.
- Call LLM (DecisionOutput → {summary_banknifty, trade}).
- Flip/No-trade exit enforcement: if holding and LLM flips side or says “No trade” → force flatten at market price (hourly close).
- Update logs.
11:15 → 15:15 — Subsequent intrahours
- Same as 10:15 loop.
15:30 — Close
- Simulate last 15 minutes (15:15 → 15:30) on 1-minute data.
- Log state change once; if exited → update memory + reset.
- LLM close plan (schema TradePlan).
- If holding and plan flips/no-trade → force flatten at close.
- Otherwise carry overnight (state remains open).
- Save checkpoint after each timestamp.

Trade simulation rules

simulate_trade_from_signal(df, trade, dt_col, state, lookback_minutes):

Trade schema (TradePlan):
- status: "Trade" or "No trade"
- type: "long" | "short" | "none"
- entry_at, target, stoploss: numbers (positive; 0 if No trade)
Entry is limit-style: if entry_at in [low, high] of a 1-min bar → entry fills at entry_at.
Exit resolution when both target & stoploss could be hit in same bar: use tiebreaker (engine uses “stoploss_first”).
P&L (pnl_pct): signed percentage vs entry (long positive if exit > entry; short inverted).
Flip/No-trade handling in engine:
- If open_position and LLM plan flips or says No trade at the tick → force flatten at minutes close (market price) and log memory.

LLM JSON schemas

All Pydantic models enforce extra="forbid" so the model can’t invent fields. The client (app/llm.py) sanitizes the schema name and forces additionalProperties: false at the root and nested objects, satisfying strict servers.

SummaryMorning (example)

class SummaryMorning(BaseModel):
    major_concern_nifty50: str
    trade_reasoning_nifty50: str
    trade_strategy_nifty50: str
    major_concern_banknifty: str
    trade_reasoning_banknifty: str
    trade_strategy_banknifty: str
    model_config = {"extra": "forbid"}

TradePlan

class TradePlan(BaseModel):
    status: Literal["No trade", "Trade"]
    brief_reason: str
    type: Literal["long", "short", "none"]
    entry_at: float
    target: float
    stoploss: float
    model_config = {"extra": "forbid"}

DecisionOutput

class SummaryBankNifty(BaseModel):
    major_concern: str
    sentiment: Literal["bullish", "bearish"]
    reasoning: str
    trade_strategy: str
    news_summary: str
    model_config = {"extra": "forbid"}

class DecisionOutput(BaseModel):
    summary_banknifty: SummaryBankNifty
    trade: TradePlan
    model_config = {"extra": "forbid"}

Outputs

Out dir (--out-dir):

stats_log.xlsx – time series of state snapshots/closing stats (one row when state changes; final close row).
trade_log.xlsx – model trade plans over time.
expert_log.xlsx – morning summaries (one per day).
summary_log.xlsx – per-hour summaries.

Checkpoint dir (--ckpt-dir or <out-dir>/checkpoint):

Parquets for each log, plus checkpoint.json (state & last timestamp).

Datasets:

parthpatel01
/

dataset

BankNifty Strategy Engine — README

Table of contents

Features

Project structure

Data inputs & expected columns

1. BankNifty hourly (`df_bn_hourly`)

2. BankNifty 1-minute (`df_bn_1m`)

3. Nifty daily or daily-like context (`df_nifty_daily`)

4. Sentiment predictions (`df_sentiment`)

5. Expert transcript (`df_transcript`)

6. News with summaries (`df_news`)

Installation

Configuration (.env + config classes)

Running

Resumable checkpoints

How the engine works (timeline)

Trade simulation rules

LLM JSON schemas

SummaryMorning (example)

TradePlan

DecisionOutput

Outputs

BankNifty Strategy Engine — README

Table of contents

Features

Project structure

Data inputs & expected columns

1. BankNifty hourly (df_bn_hourly)

2. BankNifty 1-minute (df_bn_1m)

3. Nifty daily or daily-like context (df_nifty_daily)

4. Sentiment predictions (df_sentiment)

5. Expert transcript (df_transcript)

6. News with summaries (df_news)

Installation

Configuration (.env + config classes)

Running

Resumable checkpoints

How the engine works (timeline)

Trade simulation rules

LLM JSON schemas

SummaryMorning (example)

TradePlan

DecisionOutput

Outputs

1. BankNifty hourly (`df_bn_hourly`)

2. BankNifty 1-minute (`df_bn_1m`)

3. Nifty daily or daily-like context (`df_nifty_daily`)

4. Sentiment predictions (`df_sentiment`)

5. Expert transcript (`df_transcript`)

6. News with summaries (`df_news`)