Execution

Tradeboard Execution Agentic Skills for Agentic Coding Tools

A comprehensive agent-skill package covering the full Tradeboard Python SDK surface, trading execution, custom limit-order algorithms, scanners, visualization, backtesting, charting, real-time WebSocket streaming, and Telegram / WhatsApp alerts for Indian markets (NSE, BSE, NFO, BFO, CDS, BCD, MCX, NCO). Works with 40+ AI coding agents via skills.sh, including Claude Code, Cursor, Codex, OpenCode, Cline, Windsurf, GitHub Copilot, Gemini CLI, Roo Code, and more.

Broker-agnostic by design: one Python SDK targets the common interface exposed by 36 broker plugins (34 securities brokers, Delta Exchange for crypto derivatives, and a Dhan sandbox plugin). Optional capabilities still depend on the active plugin and account. The skill is response-aware: every reference doc and example demonstrates how to chain endpoints together (e.g. place order → poll status → read fill price → compute SL → attach SL+target → alert) so the agent can write complete strategies, not just isolated API calls.

Quick Install

Install the skill into your project using npx skills. The CLI auto-detects your AI coding agent and installs skills to the correct directory.

# GitHub shorthand
npx skills add wesoftcorp/tradeboard-docs-skills

# Full GitHub URL
npx skills add https://github.com/wesoftcorp/tradeboard-skills

Install the bundled skill explicitly:

npx skills add wesoftcorp/tradeboard-docs-skills -s tradeboard

List available skills before installing:

npx skills add wesoftcorp/tradeboard-docs-skills -l

Install globally (available across all projects):

npx skills add wesoftcorp/tradeboard-docs-skills -g

Supported AI Coding Agents

Skills are installed via skills.sh which supports 40+ agents. Each agent reads skills from its own directory:

Agent	Skills Directory
Claude Code	.claude/skills/
Cursor	.agents/skills/
Codex	.agents/skills/
OpenCode	.agents/skills/
Cline	.agents/skills/
Windsurf	.agents/skills/
GitHub Copilot	.agents/skills/
Gemini CLI	.agents/skills/
Roo Code	.agents/skills/
+ 30 more	Auto-detected by npx skills

The npx skills add command detects which agents you have installed and places the skill files in the correct paths automatically.

Supported Use Cases

Use Case	What's Covered
Order Execution	Equity, F&O, options-by-offset (ATM/ITM/OTM), multi-leg (iron condor, straddle, diagonal), basket, split, smart-order, GTT (OCO + single-trigger)
Custom Execution Algos	Limit-order chaser (peg the touch, auto-modify, MARKET fallback), TWAP slicer, iceberg slicer, time-based cancel, price-based cancel-and-replace, conditional bracket
Scanners	Top gainers / losers, breakout (with volume confirmation), RSI oversold, volume surge, pre-open gap up / down, F&O OI delta, built on multiquotes + history pipelines
Visualization	Sector heatmap, YTD / CAGR heatmaps, OI histogram, PCR dashboard, seasonality
Backtesting	vectorbt glue with realistic Indian-market fees, NIFTY benchmark, plain-language reports, direct DuckDB Historify access for bulk multi-symbol pulls
Charting	Candlestick (no weekend gaps), option-chain OI, max-pain, IV smile, depth ladder
Real-Time Streaming	LTP / Quote / Depth WebSocket (Mode 1/2/3), depth_level 5/20/30/50, verbose control, persistent reconnect loop, callback router for per-symbol handlers
Alerts	Telegram + WhatsApp send-only endpoints with rich pre-built templates (order placed, fill, SL hit, target hit, position closed, scanner results, daily P&L, error)

Capabilities

How the Skill Triggers

The bundled skill is named tradeboard. After installation it auto-loads in your agent when the conversation matches Tradeboard-related work. Common trigger phrases:

• "Place an order on Tradeboard"
• "Build a limit-order chaser for SBIN"
• "Scan NIFTY 50 for breakouts / gainers / RSI oversold"
• "Stream NIFTY depth with a 20-level book"
• "Backtest an EMA crossover on Historify data"
• "Render the option chain OI chart"
• "Send a Telegram alert when NIFTY crosses 26000"
• "Place an ATM straddle with auto stoploss on each leg"
• "Fetch Greeks and pick the 25-delta strike"

The agent then reads SKILL.md for safety rules and the API surface table, and consults the deep-dive references on demand for parameter-complete API docs.

Helper Scripts (16 Composable Modules)

Script	Purpose
tradeboard_client.py	get_client(): bootstraps from .env via find_dotenv(); resolves TRADEBOARD_API_KEY, host, ws_url, Historify DuckDB path
symbols.py	Build / parse equity, futures, options symbols; common index lists; client.symbol and client.search wrappers
lotsize.py	F&O lot validation from bundled LotSize.csv; nearest_lot() + authoritative live lookup via client.symbol(...).lotsize
orders.py	preview_order, place_with_retry, modify_with_retry, cancel_with_retry, poll_until_terminal, handles rate limits
execution.py	LimitChaser, TWAPSlicer, IcebergSlicer, composable execution-algo primitives
responses.py	Response navigation helpers: extract_orderid, avg_fill_price, poll_until_filled, extract_ltp, extract_touch, etc.
workflows.py	High-level chains: place_with_sl_target, enter_options_atm_with_sl, square_off_with_alert, place_smart_with_position_check
option_analytics.py	chain_to_df, pcr, max_pain, iv_skew, payoff (multi-leg P&L diagram)
scanner.py	Scanner: multi-symbol filter pipeline over multiquotes + history, parallelized with thread pool
stream.py	subscribe() context manager, run_until_interrupt, CallbackRouter, reconnect_loop
plotting.py	Candlestick (no weekend gaps), OI histogram, heatmap, depth ladder, payoff chart, all Plotly dark theme
duckdb_data.py	Direct Historify access for bulk pulls (load_ohlcv, load_multi, resample_ist, list_symbols, date_range)
alerts.py	Telegram + WhatsApp dispatcher with rich fmt_* templates and one-call notify(via=...) and alert_order_lifecycle
fees.py	Indian-market cost model (delivery / intraday / F&O futures / F&O options / currency / commodity), fees_pct + fixed_fees_inr
ta_helpers.py	Ergonomic wrappers: TA-Lib (EMA, SMA, RSI, MACD, ATR, BBANDS, ADX) + tradeboard.ta (Supertrend, Donchian, Ichimoku, HMA, KAMA)
trade_logger.py	CsvJournal and SqliteJournal with identical .write() surface, persistent trade journals

Knowledge Base (19 Reference Files)

Every SDK endpoint has a dedicated reference with Request / Success-Response / Error-Response / Field-extraction / Chains-with sections. The response shapes are documented inline so the agent knows exactly which fields to read and where the chain goes next.

Category	What's Covered
Order Management	placeorder, placesmartorder, optionsorder, optionsmultiorder, basketorder, splitorder, modifyorder, cancelorder, cancelallorder, closeposition, GTT family
Order Information	orderstatus, openposition, the polling pattern and data.average_price extraction
Market Data	quotes, multiquotes, depth, history (REST + Historify DuckDB), intervals
Symbol Services	symbol, search, expiry, instruments, lot sizes, freeze quantities, tick sizes
Options Services	optionsymbol (ATM / ITMn / OTMn resolution), optionchain, syntheticfuture, optiongreeks (delta / gamma / theta / vega / rho + IV)
Account Services	funds, margin (multi-leg margin calculator), orderbook, tradebook, positionbook, holdings
Market Calendar	holidays(year) and timings(date), schedule-aware strategy startup; timing data identifies closed sessions
Analyzer (Sandbox) Mode	analyzerstatus, analyzertoggle(mode=True), iterate safely before going live
WebSocket Streaming	Modes 1 (LTP) / 2 (Quote) / 3 (Depth with depth_level 5/20/30/50), verbose 0/1/2, heartbeat, reconnect
Alerts	telegram(username, message) and full whatsapp(text, to=..., image=..., document=...) surface incl. broadcast (≤5) and slash-command receiving
Indicators	TA-Lib mandatory for standard set; tradeboard.ta for Supertrend / Donchian / Ichimoku / HMA / KAMA / ALMA / ZLEMA / VWMA + exrem / crossover / flip
Execution Algorithms	Limit chaser algorithm + TWAP + iceberg primitives, plus patterns for time-cancel, price-replace, conditional bracket
DuckDB Historify	Direct read-only access to <tradeboard>/db/historify.duckdb; bulk multi-symbol pulls; NSE 09:15 IST-aligned resampling
Symbol Format	Equity (RELIANCE), Futures (NIFTY30JUN26FUT), Options (NIFTY30JUN2626500CE) + full index symbol lists (NSE_INDEX, BSE_INDEX, GLOBAL_INDEX, MCX_INDEX)
F&O Lot Sizes	Bundled LotSize.csv (Apr / May / Jun 2026 SEBI snapshot); live client.symbol(...).lotsize for authoritative value
Order Constants	Exchange / Product / Price-type / Action / Validity / Offset / WS mode / Verbose level, all enums
Rate Limits	Order APIs 10/sec, smart orders 2/sec, general 50/sec, webhooks 100/min, retry-on-429 helper
Common Workflows	10 end-to-end response-chained recipes covering the canonical patterns
Error Codes	Common SDK errors and step-by-step fixes (invalid key, session expired, lot violation, insufficient margin, etc.)

Examples Catalog (33 Production-Ready Scripts)

Folder	Scripts	Coverage
01_execution	6	Equity LIMIT with quote-anchored pricing, ATM straddle with auto-SL, iron condor with margin pre-check, basket rebalance, SL+target workflow, Supertrend live
02_scanners	5	NIFTY 50 gainers / losers, 20-day breakout (with volume confirmation), RSI oversold, volume surge, pre-open gap
03_visualization	4	Sector heatmap (treemap), YTD heatmap, OI change histogram, PCR dashboard
04_backtesting	3	EMA crossover, Supertrend, multi-symbol screener, all with NIFTY benchmark, Indian fees, plain-language reports
05_charting	4	Candlestick with EMA + Supertrend overlays, option-chain OI, max-pain profile, market-depth ladder
06_streaming	4	Basic LTP stream, 20-level depth stream with parquet logging, Telegram alert on price breach, persistent reconnect loop
07_execution_algos	7	Limit chaser, TWAP slicer, iceberg slicer, time-based cancel, price-based cancel-and-replace, conditional bracket, cancel-all-with-alert

Prerequisites

1. AI Coding Agent

Install any supported AI coding agent. For example:

• Claude Code: npm install -g @anthropic-ai/claude-code
• Cursor: Desktop IDE with built-in AI
• Codex: npm install -g @openai/codex
• OpenCode: go install github.com/opencode-ai/opencode@latest
• Cline: VS Code extension
• Windsurf: Desktop IDE with AI
• Or any of the 40+ supported agents

Then install the skill:

npx skills add wesoftcorp/tradeboard-docs-skills

2. Tradeboard Instance

Order placement, real-time data, and most other endpoints require a running Tradeboard instance:

git clone https://github.com/wesoftcorp/tradeboard-docs.git
cd tradeboard
pip install uv && uv run app.py

Tradeboard runs locally at http://127.0.0.1:5000 (REST) and ws://127.0.0.1:8765 (WebSocket). You need a broker account connected via Tradeboard and an API key from the dashboard. See the Tradeboard documentation for installation and broker setup.

Backtesting-only? If you intend to use this skill purely for backtesting against Historify market data, you can skip the broker session; just point HISTORIFY_DUCKDB_PATH at a populated historify.duckdb file.

3. Python Environment Setup

python -m venv venv
source venv/bin/activate           # Linux / Mac
# venv\Scripts\activate             # Windows

# Install TA-Lib C library first (required for the tradeboard[indicators] extra)
brew install ta-lib                 # macOS
# sudo apt install libta-lib-dev    # Linux

# Install all Python packages
pip install -r requirements.txt

requirements.txt ships with:

• tradeboard[indicators]: SDK + JIT-accelerated tradeboard.ta indicators
• python-dotenv, pandas, numpy, duckdb, pyarrow
• TA-Lib for standard indicators
• vectorbt for backtesting (composes with the vectorbt-backtesting-skills package)
• plotly, matplotlib, seaborn for charting
• APScheduler, pytz for scheduled / intraday loops

4. Configure API Keys

cp .env.sample .env
# Edit .env with your Tradeboard credentials

The .env file supports:

# Tradeboard REST + WebSocket
TRADEBOARD_API_KEY=your_tradeboard_api_key_here
TRADEBOARD_HOST=http://127.0.0.1:5000
TRADEBOARD_WS_URL=ws://127.0.0.1:8765

# Optional: direct Historify DuckDB access (bulk pulls for backtests / heatmaps)
HISTORIFY_DUCKDB_PATH=/srv/tradeboard/db/historify.duckdb

# Optional: default strategy tag for order book + analyzer logs
TRADEBOARD_DEFAULT_STRATEGY=python

# Optional: default alert destinations
ALERT_WHATSAPP_TO=919876543210
ALERT_TELEGRAM_USERNAME=your_tradeboard_loginid

Usage Examples

The skill is single-skill (no slash commands); interact with it through natural language. The agent loads the relevant references and helpers on demand.

Order Execution

> Place a market buy for 10 RELIANCE on NSE under strategy "python".
> Place an ATM straddle on NIFTY for 30JUN26 expiry with 30% SL on each leg.
> Build an iron condor on NIFTY 30JUN26 with OTM4 short wings and OTM6 long wings.
> Rebalance my portfolio to equal-weight across these 8 symbols.

Custom Execution Algorithms

> Build a limit-order chaser for SBIN: buy 50 shares, peg the bid,
  modify when bid moves up, convert to MARKET after 2 minutes if unfilled.

> Slice 1000 shares of SBIN as a TWAP across 5 children over 5 minutes.

> Run an iceberg buy on HDFCBANK: 200 shares total, show only 25 at a time,
  fixed limit price.

> Place a LIMIT buy at Rs 750 and cancel it if it is not filled in 60 seconds.

> Place a market buy, wait for the LTP to move 0.3% from fill,
  then attach an SL-M at 0.8% below fill.

Scanners

> Show me NIFTY 50 gainers above 1% with volume confirmation.
> Run an RSI oversold scan on NIFTY 100 for RSI(14) <= 30 and downtrending.
> Find symbols with volume >= 3x their 20-day average.
> Show pre-open gap up symbols above 1.5%.

Visualization & Charting

> Render a sector heatmap for NIFTY 50, treemap-style.
> Plot NIFTY's option-chain OI histogram for 30JUN26 expiry, 100-point grid.
> Generate a max-pain chart for BANKNIFTY 30JUN26.
> Show me a candlestick chart for SBIN 5m with EMA 20/50 and Supertrend overlays.

Backtesting (with vectorbt)

> Backtest EMA(10)/EMA(20) crossover on SBIN, daily, 3 years.
> Backtest Supertrend(10, 3) on RELIANCE with realistic delivery fees.
> Run an EMA crossover backtest across NIFTY 50 and rank by Sharpe.

Real-Time Streaming

> Stream LTP for NIFTY, BANKNIFTY, RELIANCE, SBIN.
> Subscribe to 20-level depth for RELIANCE and log every tick to parquet.
> Watch NIFTY and alert via Telegram when it crosses 26000 or 25500.
> Start a persistent quote stream with auto-reconnect.

Alerts

> Send a Telegram alert: "Stoploss hit on BANKNIFTY 58000 PE @ Rs 220".
> Send the daily P&L report to WhatsApp.
> Send the breakout scanner results CSV to my phone.

Key Features

Response-Aware Design (The Canonical Chain)

Every reference doc and example chains endpoints together. The agent knows not just how to call an endpoint, but how to read the response and feed the right field into the next step. The canonical example is automatic SL and target on the actual fill price:

from scripts.workflows import place_with_sl_target

result = place_with_sl_target(
    client,
    strategy="python",
    symbol="RELIANCE", exchange="NSE",
    action="BUY", quantity=10, product="MIS",
    price_type="MARKET",
    sl_pct=1.0,           # 1% below fill
    target_pct=2.0,       # 2% above fill (1:2 R/R)
    alert_via=("telegram", "whatsapp"),
)

print(result.entry_avg_price)   # actual fill, not intended entry
print(result.sl_order_id)        # SL-M placed at fill * 0.99
print(result.target_order_id)    # LIMIT placed at fill * 1.02

Under the hood: placeorder → orderstatus(poll) → data.average_price → compute SL+target → placeorder(SL-M) → placeorder(LIMIT) → alert. All wrapped in one call. All responses validated.

Comprehensive Telegram + WhatsApp Alerts

Pre-built templates for every common event:

Event	Template Function
Order placed	fmt_order_placed(...)
Order filled	fmt_order_filled(...)
Stoploss triggered	fmt_stoploss_triggered(...)
Target hit	fmt_target_hit(...)
Position closed	fmt_position_closed(...)
Scanner results	fmt_scanner_results(...)
Daily P&L	fmt_daily_pnl(...)
Error	fmt_error(...)

One-call multi-channel dispatch via notify(client, message, via=("telegram", "whatsapp")). WhatsApp supports text, image with caption, and document (PDF / CSV) attachments. Broadcasts up to 5 recipients. All failures degrade gracefully: alert errors never crash the strategy.

Custom Execution Algorithms: First-Class

Three reusable execution-algo primitives with full configurability:

# 1. Peg the touch, modify on move, MARKET fallback
from scripts.execution import LimitChaser, ChaserConfig

LimitChaser(client, ChaserConfig(
    symbol="RELIANCE", exchange="NSE", action="BUY",
    quantity=10, product="MIS", strategy="chaser",
    tick_size=0.05, timeout_sec=120, on_timeout="market",
)).run()

# 2. Slice parent into N equal time-spaced children
from scripts.execution import TWAPSlicer, TWAPConfig

TWAPSlicer(client, TWAPConfig(
    symbol="SBIN", exchange="NSE", action="BUY",
    total_quantity=1000, slices=10, duration_sec=600,
    product="MIS", strategy="twap",
)).run()

# 3. Show only display_quantity at a fixed limit
from scripts.execution import IcebergSlicer, IcebergConfig

IcebergSlicer(client, IcebergConfig(
    symbol="HDFCBANK", exchange="NSE", action="BUY",
    total_quantity=5000, display_quantity=500,
    price=1820.00, product="MIS", strategy="iceberg",
)).run()

Plus standalone patterns for time-based cancel, price-based cancel-and-replace, and conditional bracket (wait for X% move, then attach SL).

Direct DuckDB Historify Access

client.history(..., source="db") works via REST, but for bulk multi-symbol pulls or full-lookback backtests, hit the DuckDB file directly:

from scripts.duckdb_data import load_ohlcv, load_multi, resample_ist

# One symbol, full history
df = load_ohlcv("SBIN", "NSE", "2020-01-01", "2026-05-24")

# 50 symbols, one query, wide DataFrame
close = load_multi(NIFTY50, "NSE", "2024-01-01", "2026-05-24", field="close")

# 5-minute bars aligned to NSE 09:15 IST
m5 = resample_ist(df_1min, "5min")

Performance: 50 symbols × 1 year of daily completes in ~50ms direct vs ~15s via REST.

Sandbox / Analyzer Mode

Iterate without sending real orders to the broker. Toggle at the top of any strategy:

client.analyzertoggle(mode=True)   # simulated
# ... develop / test ...
client.analyzertoggle(mode=False)  # live

The SDK returns fake orderids and logs every call to the analyzer log (viewable at /analyzer). Sandbox has ₹1 Crore default capital and exchange-aligned auto-square-off.

Indian-Market Cost Model

Realistic fees by segment with a Zerodha-style fee table, configurable for any broker:

Segment	fees	fixed_fees
Equity Delivery	0.00111 (0.111%)	Rs 0/order
Equity Intraday	0.000225 (0.0225%)	Rs 20/order
F&O Futures	0.00018 (0.018%)	Rs 20/order
F&O Options	0.00098 (0.098%)	Rs 20/order
Currency Futures	0.00009 (0.009%)	Rs 20/order
Currency Options	0.00098 (0.098%)	Rs 20/order
Commodity Futures	0.00018 (0.018%)	Rs 20/order
Commodity Options	0.00098 (0.098%)	Rs 20/order

scripts/fees.py exposes fees_pct(segment) and fixed_fees_inr(segment) for direct use in vectorbt.Portfolio.from_signals(fees=..., fixed_fees=...). estimate_charges(...) returns a full breakdown (STT, exchange txn, SEBI, GST, stamp) for live order preview.

F&O Lot Sizes (SEBI Snapshot)

Index	Lot Size (Jun 2026)	Exchange
NIFTY 50	65	NFO
Nifty Bank	30	NFO
Nifty Financial Services	60	NFO
Nifty Midcap Select	120	NFO
Nifty Next 50	25	NFO

Bundled in assets/LotSize.csv (~200 underlyings, Apr / May / Jun 2026 columns). client.symbol(symbol, exchange='NFO').data.lotsize returns the authoritative live value when the snapshot is stale.

File-Output Convention

Generated scripts and outputs land in a per-action subfolder, vectorbt-style, created on demand:

tradeboard_workspace/
├── execution/atm_straddle/         # straddle.py, journal.csv
├── execution_algos/chase_reliance/ # chaser.py, fills.csv
├── scanners/breakout/              # scan.py, results_2026-05-24.csv
├── backtesting/supertrend_sbin/    # backtest.py, equity.html, trades.csv
├── charting/nifty_oi_30jun26/      # chart.py, oi.html
└── streaming/nifty_depth/          # stream.py, ticks.parquet

Each subfolder is self-contained, script, generated data, plots, logs. The user can move, share, or rm -rf any single experiment without disturbing the rest.

Rate-Limit Handling

Order APIs are capped at 10/sec (smart orders 2/sec); general data APIs at 50/sec. The helper layer retries transparently:

from scripts.orders import place_with_retry, modify_with_retry, cancel_with_retry

# 3 retries with widening cooldown (0.5s, 1.5s, 3.5s)
resp = place_with_retry(client, strategy=..., symbol=..., ...)

Scanners auto-batch via multiquotes (one call for many symbols) rather than looping quotes.

Project Structure

.
├── README.md
├── package.json                       # npm-installable via skills.sh
├── .env.sample                        # API keys + Historify path
├── requirements.txt
└── skills/
    └── tradeboard/
        ├── SKILL.md                   # Entry: safety rules, surface table, quick templates
        │
        ├── references/                # 19 parameter-complete deep-dives
        │   ├── order-management.md
        │   ├── order-information.md
        │   ├── market-data.md
        │   ├── symbol-services.md
        │   ├── options-services.md
        │   ├── account-services.md
        │   ├── market-calendar.md
        │   ├── analyzer-services.md
        │   ├── websocket-streaming.md
        │   ├── alerts.md
        │   ├── indicators.md
        │   ├── execution-algos.md
        │   ├── duckdb-historify.md
        │   ├── symbol-format.md
        │   ├── lot-sizes.md
        │   ├── order-constants.md
        │   ├── rate-limits.md
        │   ├── common-workflows.md
        │   └── error-codes.md
        │
        ├── scripts/                   # 16 composable helpers
        │   ├── tradeboard_client.py
        │   ├── symbols.py
        │   ├── lotsize.py
        │   ├── orders.py
        │   ├── execution.py           # LimitChaser, TWAPSlicer, IcebergSlicer
        │   ├── responses.py           # Response-navigation extractors
        │   ├── workflows.py           # place_with_sl_target, enter_options_atm_with_sl, ...
        │   ├── option_analytics.py
        │   ├── scanner.py
        │   ├── stream.py
        │   ├── plotting.py
        │   ├── duckdb_data.py
        │   ├── alerts.py
        │   ├── fees.py
        │   ├── ta_helpers.py
        │   └── trade_logger.py
        │
        ├── examples/                  # 33 production-ready scripts
        │   ├── 01_execution/          (6 files)
        │   ├── 02_scanners/           (5 files)
        │   ├── 03_visualization/      (4 files)
        │   ├── 04_backtesting/        (3 files)
        │   ├── 05_charting/           (4 files)
        │   ├── 06_streaming/          (4 files)
        │   └── 07_execution_algos/    (7 files)
        │
        └── assets/
            └── LotSize.csv            # F&O snapshot (Apr/May/Jun 2026)

Reference Files

Reference File	Description
order-management.md	placeorder, smart, options, multi-leg, basket, split, modify, cancel, close, GTT, full request / response
order-information.md	orderstatus, openposition, polling loop, data.average_price extraction
market-data.md	quotes, multiquotes, depth, history, intervals, REST and DuckDB-backed
symbol-services.md	symbol, search, expiry, instruments, lot sizes, freeze quantities, broker tokens
options-services.md	optionsymbol, optionchain, syntheticfuture, optiongreeks, ATM/ITM/OTM offsets, full Greek chain
account-services.md	funds, margin (multi-leg), orderbook, tradebook, positionbook, holdings
market-calendar.md	holidays(year) and timings(date), schedule-aware startup
analyzer-services.md	analyzerstatus, analyzertoggle(mode=True), sandbox iteration
websocket-streaming.md	Modes 1/2/3, depth_level 5/20/30/50, verbose levels, heartbeat, reconnect
alerts.md	telegram and whatsapp send-only endpoints with full template library and slash-command receiving
indicators.md	TA-Lib + tradeboard.ta complete reference with signal-cleaning patterns
execution-algos.md	Limit chaser, TWAP, iceberg algorithms, building blocks for custom execution strategies
duckdb-historify.md	Direct DuckDB schema, single-symbol load, multi-symbol wide load, IST-aligned resampling
symbol-format.md	Equity / Futures / Options grammar + full index symbol lists (NSE_INDEX, BSE_INDEX, GLOBAL_INDEX, MCX_INDEX, NCO)
lot-sizes.md	F&O lot-size CSV + live client.symbol lookup, validation, sizing patterns
order-constants.md	All enum values (exchange, product, price type, action, validity, offset, WS mode, verbose level)
rate-limits.md	Per-bucket limits, retry-on-429 helper, scanner batching guidance
common-workflows.md	10 canonical response-chained recipes (order→SL→target, scanner→alert, etc.)
error-codes.md	Common SDK errors and step-by-step fixes

All three share the same .env convention, file-output layout, and Indian-market cost model. You can install them side by side.

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