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Algo Trading Disclosure To Exchange Membership

Community
HimanshuJ16
algo-trading-disclosure-to-exchange-membership

Use when an order is generated or parameterised by an algorithm and the venue or regulator requires algorithm identification, registration or disclosure; blocks orders whose exchange-facing metadata is missing or stale, before FIX serialisation.

Overview

PublisherHimanshuJ16
RepositoryAlgo-Trading-Skills
Skill namealgo-trading-disclosure-to-exchange-membership
Stars
50
Forks
7
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by HimanshuJ16 on GitHub. Read the source before you install it.

Installation

Install the Algo Trading Disclosure To Exchange Membership AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HimanshuJ16/Algo-Trading-Skills/tree/main/skills/algo-trading-disclosure-to-exchange-membership
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HimanshuJ16/Algo-Trading-Skills.git /tmp/Algo-Trading-Skills
mkdir -p .claude/skills
cp -r /tmp/Algo-Trading-Skills/skills/algo-trading-disclosure-to-exchange-membership .claude/skills/algo-trading-disclosure-to-exchange-membership
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Algo Trading Disclosure To Exchange Membership in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Algo Trading Disclosure To Exchange Membership on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Algo Trading Disclosure To Exchange Membership is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Purpose and Scope

Use this skill to implement the compliance gate between order generation and the venue adapter. It validates the metadata that will actually leave the trading system; it does not determine legal classification or replace a jurisdictional legal review.

The control supports a registry containing simple statuses or richer venue-scoped, version-scoped AlgoRegistration records. It returns a structured OrderComplianceReport with a stable reason_code for audit and alert routing.

When to Use

Use this skill when an order may be generated or materially parameterized by an algorithm and the venue, broker, or regulator requires algorithm identification, registration, or disclosure controls. Apply it before FIX serialization and again at the child-order boundary if a smart order router can create new outbound orders.

Regulatory requirements are not uniform:

  • In the EU, MiFID II and RTS 6 require firms and venues to identify and control algorithmic trading activity, while the exact order flag, algorithm identifier, and transaction-reporting fields depend on the venue and reporting path.
  • In India, SEBI and exchange operating procedures can require a unique exchange-issued identifier on algo orders. Configure the current exchange rule, not an EU assumption, in the registry and venue adapter.

When NOT to Use

Do not use this skill as the sole control for position limits, price collars, message-rate limits, market-abuse surveillance, transaction reporting, or FIX session controls. Combine it with the dedicated risk and reporting skills.

Do not infer that every EU automated order needs one universal pre-registered identifier. A pure routing system may fall outside the MiFID II definition of algorithmic trading, and venue-specific tagging rules still require confirmation. Manual orders should use the separate trader or short-code control path.

Prerequisites

  • Confirmed jurisdiction, venue, broker, order-routing path, and current venue tagging specification.
  • A version-controlled registry with approval status, effective ownership, and optional approved venues and algorithm version.
  • A stable upstream classification for is_algorithmic with documented legal ownership; do not derive it from the presence of algo_id.
  • A pre-FIX interception point and a venue adapter test that proves the approved identifier survives serialization into the actual outbound tag.
  • Durable compliance logs and an alert route for hard rejects, registry outages, and attempted stale deployments.
  • Python 3.10+ for the reference implementation.

Inputs and Outputs

OutboundOrder requires order_id, symbol, and an explicit is_algorithmic value. It can carry algo_id, trader_id, venue, algo_version, and parent_algo_id.

AlgoRegistration supports status, optional venues, and optional version. A plain registry value such as {"VWAP_V2.0": "APPROVED"} remains supported. venues must be a collection of venue codes; a bare string is rejected because it would otherwise iterate per character and widen the approved scope.

The engine validates the whole registry at construction and raises on a malformed, blank, or duplicate entry, so a bad registry fails at deployment rather than at order time. The validated snapshot is exposed read-only, and evaluate_order returns an OrderComplianceReport without sending or mutating an order.

Matching rules are deliberately asymmetric: venue codes are compared case-insensitively, while algo_id and algo_version must match the registry exactly. Both directions fail closed.

Workflow

  1. Resolve the applicable rule: identify the venue and jurisdiction and document whether the order requires algorithm disclosure, a venue-issued identifier, a transaction-reporting identifier, or more than one field.
  2. Classify the order upstream: set is_algorithmic from the approved classification service. Do not treat a missing algo_id as evidence that an order is manual.
  3. Validate the order envelope: reject blank identifiers, invalid field types, manual orders without trader_id, and manual orders that carry any algorithmic metadata — algo_id, parent_algo_id, or algo_version. A manual order carrying algorithm lineage is a classification defect upstream, not a field to ignore.
  4. Validate algorithm disclosure: for an algorithmic order, require the exchange-facing algo_id, look it up in the immutable registry, and require status APPROVED.
  5. Apply scope checks: if the registration is venue- or version-scoped, require an exact match. If a child order supplies parent_algo_id, require it to match the child algo_id.
  6. Gate and record: allow only compliant orders to reach the venue adapter. Hard-reject all other orders, persist the reason code and registry status, and alert on repeated or unknown identifiers.
  7. Verify the wire payload: in integration tests and deployment checks, inspect the serialized FIX or broker payload. The in-memory field alone does not prove that the venue received the required tag.

Failure Modes and Recovery

  • Unknown, pending, deprecated, or suspended ID: hard-reject, alert, and restore the last approved registry or deployment; never auto-approve.
  • Registry unavailable or malformed: fail closed, preserve the order intent for operator review, and recover by restoring a signed, versioned snapshot.
  • Venue or version mismatch: stop the affected route, verify the approval record and deployment manifest, then redeploy only after the registry and venue adapter agree.
  • Child tag missing or changed: cancel or prevent further child orders, reconcile already-submitted orders, and fix propagation before reopening flow.
  • Unexpected reject-rate spike: activate the operational kill switch for the affected strategy or venue, inspect reason-code metrics, and roll back the most recent registry or adapter change.

Common Pitfalls

  • Treating MiFID II, SEBI, and individual exchange procedures as one universal algo_id rule.
  • Hard-coding a FIX tag number without validating the current venue or broker specification and the raw wire message.
  • Registering a strategy once, then deploying a materially changed version without an approval transition and deployment record.
  • Tagging a parent order while a smart order router emits untagged or differently tagged child orders.
  • Declaring a venue scope as a bare string (venues="XNYS") instead of a collection. A string iterates per character, so the scope silently becomes {"X", "N", "Y", "S"} — rejecting the intended venue while approving an unrelated one named X.
  • Merging registry sources without checking for keys that collapse to the same identifier once whitespace is trimmed; last-wins can silently promote a SUSPENDED algorithm to APPROVED.
  • Mutating the engine's registry in place to hot-fix an approval, bypassing the controlled deployment process and the audit record it produces.
  • Treating a missing registry entry as a temporary warning instead of a hard pre-trade failure.
  • Logging only free-text messages and losing the stable reason code needed for monitoring, audit, and incident recovery.

Verification

Run:

text
python -m unittest discover -s skills/algo-trading-disclosure-to-exchange-membership/scripts

The tests cover approved, unknown, pending, deprecated, manual, malformed, venue-scoped, version-scoped, parent/child, and invalid-input paths, plus the registry-construction failures (bare-string venue scope, duplicate keys, blank registered version, read-only snapshot). Add a venue-adapter integration test that asserts the exact outbound tag and a non-production deployment test that proves rollback restores the prior approved registry.

Related Skills

  • india-sebi-algo-trading-tagging-requirements
  • mifid-ii-algo-trading-compliance-eu
  • uk-fca-algorithmic-trading-systems-controls
  • paper-to-live-promotion-checklist
  • kill-switch-and-drawdown-circuit-breakers

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Algo Trading Disclosure To Exchange Membership AI skill do?

Use when an order is generated or parameterised by an algorithm and the venue or regulator requires algorithm identification, registration or disclosure; blocks orders whose exchange-facing metadata is missing or stale, before FIX serialisation.

Why use Algo Trading Disclosure To Exchange Membership on TypingMind?

Because you install it once and use it with any model. Algo Trading Disclosure To Exchange Membership is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Algo Trading Disclosure To Exchange Membership in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HimanshuJ16/Algo-Trading-Skills/tree/main/skills/algo-trading-disclosure-to-exchange-membership. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Algo Trading Disclosure To Exchange Membership?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Algo Trading Disclosure To Exchange Membership?

As many as you like. As long as a model supports skills, you can use Algo Trading Disclosure To Exchange Membership with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Algo Trading Disclosure To Exchange Membership AI skill free?

Yes. It is published on GitHub by HimanshuJ16 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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