fyers-trading

FYERS Trading API v3

Help developers build strategies, automation, and backtesting on the official FYERS Developer API v3 (https://api-t1.fyers.in). Generate working Python (fyers-apiv3 SDK) or raw-REST code, wire up OAuth correctly, and respect the real-money safety rules below.

Scope note. This skill targets the public developer API (api-t1.fyers.in

• the fyers-apiv3 SDK + WebSockets). It is not the FIA chat-assistant proxy (fia.fyers.in). Generate code freely; the "never write code / no orders" rules from FIA do not apply here.

Safety rules (non-negotiable — real money)

1. Secrets only via environment variables. Never hardcode app_id, secret_id, access_token, or PIN in generated code or commit them. Read from env / .env.
2. Dry-run by default. Order-placing code must default to a DRY_RUN=True (or --dry-run) mode that logs the payload instead of sending it. Live placement requires an explicit, obvious opt-in flag the user sets themselves.
3. Validate the symbol against the master before every order. Never place, modify, or build an order from a hand-constructed symbol. Confirm it exists in the daily symbol master first (scripts/fyers_symbols.py / validate_symbol()), which also gives the lot size to check qty against. An unvalidated symbol fails live with code -300. fyers_client.place_order() enforces this by default.
4. Confirm before going live. Before running anything that places/modifies/ cancels real orders, state plainly what it will do and have the user confirm.
5. Respect rate limits: 10 req/sec, 200 req/min, 100,000 req/day; order ops ≤10/sec (HTTP 429 → honor Retry-After). Breach the per-minute cap >3×/day → blocked all day.
6. Use WebSocket for live ticks, never a polling loop on /quotes.
7. Tokens expire daily. A 401 / code -8/-15/-16/-17 means re-login, not retry.

Two-gate rule for going live (elaborates rule 2 and rule 4 — doesn't replace them). Under the default conversational-execution mode, you (the agent) may run dry-run order code yourself and show the real logged payload in chat. But you may invoke a live order path yourself only when both gates hold: (a) the code/script itself requires an explicit opt-in the user set themselves (e.g. --live, DRY_RUN=False in the file/ CLI — this must exist per rule 2, and you never set it on the user's behalf), and (b) you have plain-language typed confirmation from the user in chat for that specific action (per rule 4). Neither gate substitutes for the other — a flag without chat confirmation, or chat confirmation without the flag, is not enough to execute live.

Default mode: conversational execution

Default behavior for any FYERS strategy/data/order task: run scripts for real, in the project folder, as you go — don't just generate code and hand it off. Develop in small increments: write/edit one small piece of the strategy → actually run it via Bash from the project folder → show the real output in chat → let the user react/redirect → repeat. Never dump a "finished" strategy in one shot without having executed each piece along the way. This applies to auth setup (below), fetching data, computing signals, dry-run orders, and backtests (see "Strategy deliverable convention"). Everything you run must be a real, persistent file in the project folder (see that section) — never an inline/throwaway snippet.

Opt-out: if the user explicitly asks only for code, or says not to run anything (e.g. "just give me the code", "don't run it"), fall back to generating files without executing — similar to this skill's older behavior.

Step 0 — Set up the environment (first time only)

If the user asks to "set up" the skill, or no project venv exists yet, create one and install the strategy-code dependencies before anything else:

python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

If a package fails to install, don't stop — install the rest, retry the failing one alone, and resolve it before moving on to strategy generation. Full detail, including the default package list and what each is for: references/setup.md.

Step 1 — Authenticate (do this first, conversationally)

Under the default conversational-execution mode, authenticate with the user in chat rather than just pointing them at a script. Do this in the project folder:

1. Check for a valid token yourself by running it:

   python scripts/fyers_login.py --check   # prints OK if cached token is valid
   

2. If missing/401 and .env doesn't exist yet, scaffold it yourself: create .env in the project folder with the same keys as .env.example (FYERS_APP_ID, FYERS_SECRET_ID, FYERS_REDIRECT_URI, and FYERS_PIN only if the refresh-token flow is needed) present but with blank values — never write secret values into the file, and never ask the user to paste secret values into chat. Then ask the user to open .env themselves and fill in the values, and wait for their explicit confirmation (e.g. "done" / "filled in") before doing anything else. Do not guess and continue.
3. Once confirmed, run the OAuth flow yourself:

   python scripts/fyers_login.py           # opens auth URL, exchanges code, caches token
   

   Relay the printed auth URL to the user in chat, have them log in and copy back the auth_code (or full redirect URL), and complete the exchange in the same script run.
4. Confirm success yourself — run python scripts/fyers_client.py profile and report the real result in chat (don't assert it should work).

This caches the daily access_token to ~/.fyers/token.json. The flow under the hood is: generate-authcode → user logs in → auth_code → appIdHash = SHA256("app_id:secret_id") → validate-authcode → access_token. Full detail, including the standalone/manual path for users who opt out of the conversational default: references/auth.md.

Step 2 — Route the task

User wants…	Load this reference	Use
Set up / install the skill, venv, dependencies	references/setup.md	—
Login / token / OAuth / refresh	references/auth.md	scripts/fyers_login.py
Quotes, depth, history, market status	references/market-data.md	scripts/fyers_client.py
Option chain, greeks, IV, PCR, expiry selection, ATM/ITM/OTM, max pain	references/market-data.md	scripts/fyers_client.py + scripts/option_chain.py
Place / modify / cancel / GTT / smart orders, positions	references/orders.md	scripts/fyers_client.py
Symbol strings (eq/fut/opt), look up a name → exact symbol, lot/tick/expiry	references/symbols.md	scripts/fyers_symbols.py
Live streaming (data / order / TBT sockets)	references/websocket.md	—
Backtest a strategy from historical candles	references/backtesting.md	scripts/example_strategy.py
Technical indicators (RSI, MACD, Bollinger, ATR, etc.)	references/indicators.md	scripts/indicators.py
Visualize / report a backtest's performance (tear sheet, Sharpe, drawdown, monthly returns)	references/quantstats.md	scripts/quantstats_report.py
Any endpoint path / payload / enum code	references/endpoints.md	—
Rate limits, error codes, retries	references/rate-limits.md	—

Read references on demand — don't load all of them up front. endpoints.md is the full path/field/enum-code catalog; the others are task-focused.

Step 3 — Write, verify, report

• Prefer reusing scripts/fyers_client.py (loads the cached token, adds the Authorization: app_id:access_token header, and wraps the safety/rate-limit logic).
• After writing code, verify it imports/compiles (python -m py_compile <file>), and run data-only paths against the live API when a token exists. Never run live order code to "test" it — use dry-run.
• Get enum codes exact (order type 1/2/3/4, side 1/-1, productType, segment/ exchange IDs). They're in references/endpoints.md; do not guess them.

Strategy deliverable convention

When building a strategy, bot, or automation (anything beyond a one-off query), deliver it as a self-contained folder, not a single script — and, per the default conversational-execution mode above, build it incrementally with real execution at each step, not as a one-shot file dump:

• Create a directory named for the strategy (e.g. strategies/sma_crossover/) and put all of its files inside — signal/entry logic, config, runner, requirements.txt if needed, .env (scaffolded per Step 1), and a short README.md. Never dump a multi-part strategy into one file. The venv, .env, and every module live in this one folder — all scripts are run FROM here, on these real files, for the rest of the conversation.
• Build it piece by piece: write/edit one small piece (e.g. the candle-fetch, then the signal, then the order stub) → run it for real via Bash from the strategy folder → show the actual output in chat → let the user redirect → move to the next piece. Do not write the whole strategy and then run it once at the end.
• After the strategy is written, produce a Mermaid flow diagram of the algorithm (data → signal → risk checks → order → logging) and save it in the folder (e.g. flow.mmd or embedded in the folder's README.md) so the user can review the logic at a glance. Show the diagram to the user.
• Automatically backtest once the strategy is functional — don't wait to be asked. As soon as the original strategy runs against real data, build and run a backtest yourself against FYERS historical candles (references/backtesting.md, scripts/example_strategy.py), in the strategy folder, and show the real metrics in chat (not a description of what it would show). This is the default flow, not an offer — proceed without a prompt. (Only skip if the user explicitly opted out of the agent running code, per "Default mode: conversational execution".) Put the backtest in the same strategy folder.
• Then automatically generate a QuantStats tear sheet — also without being asked. Right after the backtest produces a returns series (df["strat_ret"]), run scripts/quantstats_report.py on it (html_report() for the full HTML tear sheet, plus key_metrics() for the headline numbers in chat) so the user sees a professional view of their algo's performance — Sharpe/Sortino/drawdown/CAGR and the monthly-returns heatmap. Save the tear sheet in the strategy folder (e.g. report.html) and tell the user its path and the key metrics. Requires QuantStats installed (references/quantstats.md); if it isn't, say so and still report the backtest's own metrics. Mind the intraday→daily and "two win rates" gotchas documented in references/quantstats.md.
• After a successful first backtest, offer Supercharge Mode. Once the strategy has a working backtest with real metrics, offer to optimize it — hand off to the sibling fyers-supercharge skill, which convenes a multi-agent strategist council that debates improvements, generates optimized variants, backtests them, and searches for measurably better versions (keeping the original as the immutable baseline). Present it as: "Your strategy is ready. Unlock Supercharge Mode? Our AI team of professional strategists will analyze it, debate improvements, generate optimized variants, and recommend stronger alternatives." On "Yes", switch to fyers-supercharge. Requires that skill to be installed; it inherits all the safety rules above.
• Order dry-runs happen the same way — run the order code yourself in dry-run and show the real logged payload. Going live is gated (see the two-gate rule under the safety rules above). You can also proactively surface scripts/trade_logger.py's tail/ summary output in chat as the conversation progresses (e.g. after a dry-run or a live fill), not only when the user explicitly asks for the log.
• Repeatability: everything you write and run during the conversation must remain a real, standalone-runnable file in the strategy folder afterward (e.g. python strategies/sma_crossover/run.py) — the conversational execution is the development loop, not the only way to run the result.

Scripts

• scripts/fyers_login.py — OAuth login + daily token cache (--check, --print-token).
• scripts/fyers_client.py — reusable REST client (profile/funds/holdings/positions/ orders/quotes/history/optionchain) with dry-run order placement + 429 handling.
• scripts/fyers_symbols.py — download/cache the daily symbol master files and resolve a name → exact symbol (search / info / refresh); no token needed (public files).
• scripts/helper.py — token-free order utilities: lot/qty validation, price rounding to tick size, expiry date parsing, DTE, and order_checks() (all pre-order checks in one call).
• scripts/option_chain.py — token-free option chain helpers: parse_chain(), atm_strike(), filter_expiry(), pcr(), straddle_cost(), max_pain(); CLI: demo.
• scripts/example_strategy.py — end-to-end template: fetch candles → signal → dry-run order. Copy and adapt; flip to live only with explicit --live.
• scripts/indicators.py — TA-Lib wrappers for common indicators (SMA/EMA/WMA, Bollinger, ADX, RSI, MACD, Stochastic, CCI, momentum, ROC, OBV, A/D, ATR/NATR); lazy talib import; CLI: demo.
• scripts/quantstats_report.py — QuantStats wrappers to visualize/report a backtest's returns series: full HTML tear sheet, Sharpe/Sortino/drawdown/CAGR, monthly heatmap; intraday→daily resampling; lazy quantstats import; CLI: demo.
• scripts/trade_logger.py — append-only JSONL audit log (~/.fyers/trades.jsonl); called automatically by fyers_client.place_order() after every order attempt; exposes log_order(), log_event(), tail(n), summary(); CLI: tail [--n N] / summary.