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Algo Parameter Defaults By Instrument Liquidity Tier

Community
HimanshuJ16
algo-parameter-defaults-by-instrument-liquidity-tier

Use when initialising VWAP, TWAP or implementation shortfall execution with instrument-specific starting constraints, classifying a validated average daily volume observation into a liquidity tier and returning a versioned profile.

Overview

PublisherHimanshuJ16
RepositoryAlgo-Trading-Skills
Skill namealgo-parameter-defaults-by-instrument-liquidity-tier
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 Parameter Defaults By Instrument Liquidity Tier 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-parameter-defaults-by-instrument-liquidity-tier
  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-parameter-defaults-by-instrument-liquidity-tier .claude/skills/algo-parameter-defaults-by-instrument-liquidity-tier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Algo Parameter Defaults By Instrument Liquidity Tier 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 Parameter Defaults By Instrument Liquidity Tier 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 Parameter Defaults By Instrument Liquidity Tier 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.

When to Use

Use this skill when initializing VWAP, TWAP, or Implementation Shortfall execution with instrument-specific starting constraints. The manager classifies a validated Average Daily Volume (ADV) observation into HIGH, MEDIUM, or LOW and returns an immutable, versioned ExecutionProfile.

The profiles are defaults and caps for a downstream execution system. They are not a live liquidity model, best-execution decision, venue permission, or substitute for pre-trade risk controls.

When NOT to Use

  • Do not classify an instrument from stale, mixed-unit, split-affected, or unverified ADV data.
  • Do not use ADV alone to authorize spread crossing, market orders, participation, or urgency; current spread, depth, volatility, order size, venue state, and risk limits are required.
  • Do not use static tiers for intraday liquidity migration, news events, auctions, halts, or flash volatility without a separate market-state overlay.
  • Do not treat the illustrative 5%/10%/20% defaults as regulatory, venue, or universal institutional limits.
  • Do not conflate these internal tiers with a regulatory liquidity classification such as ESMA's annual liquid/illiquid determination, which governs transparency obligations and not execution parameters — see references/standards.md.
  • Do not bypass independent market-access, price-band, credit, position, notional, or kill-switch controls.

Prerequisites

  • A documented ADV definition: instrument, units, session calendar, corporate-action treatment, lookback, and as-of timestamp.
  • A configured high/medium threshold pair with high > medium and a maximum permitted ADV age.
  • Current market-state data for spread, depth, volatility, venue status, and order-size impact checks.
  • An EMS that can enforce participation, price, notional, credit, position, and cancellation controls independently.
  • A versioned calibration process using TCA, walk-forward evaluation, and rollback approvals.
  • Python 3.10+.

Workflow

  1. Define calibration: Set validated ADV thresholds, max_adv_age_days, profile values, and a calibration version. Store the unit and factor source outside the manager.
  2. Decide whether freshness is mandatory: adv_age_days is optional, and omitting it skips the staleness check entirely rather than failing. If the strategy cannot tolerate an unverified age, construct the manager with require_adv_age=True so a missing timestamp raises instead of classifying silently.
  3. Validate ADV: Pass a finite, non-negative ADV value and, when available, its observation age. Reject stale or malformed values; do not silently classify them as LOW. A zero ADV logs a warning and is a data-quality stop, not a genuine LOW instrument — the manager cannot distinguish "never trades" from "the feed broke".
  4. Classify the tier: Call classify_tier(adv, adv_age_days=...) or get_profile(...). Threshold boundaries are deterministic: high is inclusive, medium is inclusive below high.
  5. Retrieve the profile: Use the immutable ExecutionProfile as a starting constraint. Record its tier, algorithm, participation cap, buffer, and calibration version. Read the calibrated set through manager.profiles, a read-only mapping proxy.
  6. Read the participation cap in the right direction: The shipped ceiling widens as liquidity falls (5%/10%/20%). That is a fill-feasibility allowance for orders large relative to a thin name's volume, not a discount — impact scales with sigma * sqrt(Q/ADV), so the LOW row is the most impact-sensitive, not the most forgiving. Approach it only when completion risk demands it.
  7. Apply live gates: Before each child order, independently check current spread/depth, volatility, order size versus displayed/expected liquidity, venue status, price bands, credit, position, and parent-order limits.
  8. Handle spread crossing: cross_spread_allowed is only a profile capability. requires_live_market_check remains true; the EMS must make the actual crossing decision from current protected quotes and risk policy.
  9. Supply the urgency policy the LOW profile omits: The LOW default is IS with cross_spread_allowed=False. An IS schedule front-loads to cut timing risk, which a never-cross posture cannot do, so define outside this module who may escalate to crossing, on what residual, and at what point in the horizon. Without that policy the order rests passively and accrues timing risk while every metric here still reads healthy.
  10. Monitor and tune: Track implementation shortfall, spread capture, fill rate, participation, rejects, signaling indicators, and residual risk by tier. Retune only through versioned walk-forward/TCA review.
  11. Rollback: If TCA, data freshness, or risk metrics breach limits, restore the last approved calibration and pause affected profiles until reconciled.

Common Pitfalls

  • ADV unit mismatch: Comparing shares/day, currency/day, contracts/day, or split-unadjusted volume against the same threshold. ESMA's average daily turnover is a currency-per-day figure and is not interchangeable with a shares/day threshold.
  • Stale ADV: Applying a 30-day observation during a material corporate action, regime shift, or recent listing without an age/data-quality gate. Note that passing no adv_age_days at all disables the gate silently — require_adv_age=True is what closes that hole.
  • Reading the widening participation cap as permission: Assuming a 20% ceiling in the LOW tier means thin names tolerate more participation. Under the square-root impact law the same fraction of ADV costs more there, because those names carry higher volatility.
  • An IS profile that can never cross: Treating the LOW tier as a complete IS algorithm. It is a passive starting posture; the urgency escalation that makes IS an IS algorithm has to come from the integration.
  • False spread permission: Treating high ADV as proof that crossing the spread is cheap or safe.
  • Participation overreach: Applying a percentage cap without checking parent notional, remaining quantity, displayed depth, and market impact.
  • Mutable calibration: Returning profiles that callers can modify and silently diverge from the reviewed calibration. Profiles are frozen, the calibrated mapping is a read-only proxy, and the caller's source mapping is copied — but a profile built directly must still be validated, which is why ExecutionProfile enforces its own invariants at construction.
  • Confusing a tier with a regulatory liquidity status: An instrument ESMA classifies as having a liquid market may be LOW here, and the reverse. The tier is not evidence of a transparency obligation, and a transparency classification is not an execution parameter.
  • Treating a zero ADV as a tier: Zero volume classifies as LOW and yields a profile whose participation cap is 20% of nothing. It is a broken or suspended instrument, not a tradable one.
  • Static tiering during shocks: Keeping a normal profile during volatility, halts, auctions, or liquidity withdrawal.

Verification

Run the focused tests:

text
python -m unittest discover -s skills/algo-parameter-defaults-by-instrument-liquidity-tier/scripts

The tests cover tier boundaries on both sides of each threshold, invalid ADV and configuration, ADV freshness (including the inclusive age boundary and the require_adv_age gate), the zero-ADV warning, construction-time profile invariants, calibration versioning, immutability of both the profile and the calibrated mapping, and live-market gating metadata. Production sign-off additionally requires historical TCA, walk-forward calibration, stress scenarios, and EMS enforcement tests.

Related Skills

  • implementation-shortfall-minimization
  • participation-of-volume-pov-execution
  • liquidity-adjusted-position-sizing
  • transaction-cost-analysis-tca-integration
  • execution-algo-twap-vwap-slicing

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 Parameter Defaults By Instrument Liquidity Tier AI skill do?

Use when initialising VWAP, TWAP or implementation shortfall execution with instrument-specific starting constraints, classifying a validated average daily volume observation into a liquidity tier and returning a versioned profile.

Why use Algo Parameter Defaults By Instrument Liquidity Tier on TypingMind?

Because you install it once and use it with any model. Algo Parameter Defaults By Instrument Liquidity Tier 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 Parameter Defaults By Instrument Liquidity Tier in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HimanshuJ16/Algo-Trading-Skills/tree/main/skills/algo-parameter-defaults-by-instrument-liquidity-tier. 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 Parameter Defaults By Instrument Liquidity Tier?

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 Parameter Defaults By Instrument Liquidity Tier?

As many as you like. As long as a model supports skills, you can use Algo Parameter Defaults By Instrument Liquidity Tier with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Algo Parameter Defaults By Instrument Liquidity Tier 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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