Options Payoff logo

Options Payoff

CommunityPopular
himself65
options-payoff

Generate an interactive options payoff curve chart with dynamic parameter controls. Use this skill whenever the user shares an options position screenshot, describes an options strategy, or asks to visualize how an options trade makes or loses money. Triggers include: any mention of butterfly, spread (vertical/calendar/diagonal/ratio), straddle, strangle, condor, covered call, protective put, iron condor, or any multi-leg options structure. Also triggers when a user pastes strike prices, premiums, expiry dates, or says things like "show me the payoff", "draw the P&L curve", "what does this trade look like", or uploads a screenshot from a broker (IBKR, TastyTrade, Robinhood, etc). Always use this skill even if the user only provides partial info — extract what you can and use defaults for the rest.

Overview

Publisherhimself65
Repositoryfinance-skills
Skill nameoptions-payoff
Stars
3.3K
Forks
378
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

    Published by himself65 on GitHub. Read the source before you install it.

Installation

Install the Options Payoff 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.
  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/himself65/finance-skills.git /tmp/finance-skills
mkdir -p .claude/skills
cp -r /tmp/finance-skills/plugins/market-analysis/skills/options-payoff .claude/skills/options-payoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Options Payoff 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 Options Payoff 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 Options Payoff 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.

Options Payoff Curve Skill

Generates a fully interactive HTML widget (via visualize:show_widget) showing:

  • Expiry payoff curve (dashed gray line) — intrinsic value at expiration
  • Theoretical value curve (solid colored line) — Black-Scholes price at current DTE/IV
  • Dynamic sliders for all key parameters
  • Real-time stats: max profit, max loss, breakevens, current P&L at spot

Step 1: Extract Strategy From User Input

When the user provides a screenshot or text, extract:

FieldWhere to find itDefault if missing
Strategy typeTitle bar / leg description"custom"
UnderlyingTicker symbolSPX
Strike(s)K1, K2, K3... in title or leg tablenearest round number
Premium paid/receivedFilled price or avg price5.00
QuantityPosition size1
Multiplier100 for equity options, 100 for SPX100
ExpiryDate in title30 DTE
Spot priceCurrent underlying price (NOT strike)middle strike
IVShown in greeks panel, or estimate from vega20%
Risk-free rate4.3%

Critical for screenshots: The spot price is the CURRENT price of the underlying index/stock, NOT the strikes. Never default spot to a strike price value.

Current SPX reference price:

!`python3 -c "exec('try:\n import yfinance as yf\n p=yf.Ticker(\'^GSPC\').fast_info[\'lastPrice\']\n print(f\'SPX ≈ {p:.0f}\')\nexcept Exception:\n print(\'SPX price unavailable — check market data\')')"`

Step 2: Identify Strategy Type

Match to one of the supported strategies below, then read the corresponding section in references/strategies.md.

StrategyLegsKey Identifiers
butterflyBuy K1, Sell 2×K2, Buy K33 strikes, "Butterfly" in title
vertical_spreadBuy K1, Sell K2 (same expiry)2 strikes, debit or credit
calendar_spreadBuy far-expiry K, Sell near-expiry KSame strike, 2 expiries
iron_condorSell K2/K3, Buy K1/K4 wings4 strikes, 2 spreads
straddleBuy Call K + Buy Put KSame strike, both types
strangleBuy OTM Call + Buy OTM Put2 strikes, both OTM
covered_callLong 100 shares + Sell Call KStock + short call
naked_putSell Put KSingle leg
ratio_spreadBuy 1×K1, Sell N×K2Unequal quantities

For strategies not listed, use custom mode: decompose into individual legs and sum their P&Ls.


Step 3: Compute Payoffs

Black-Scholes Put Price

d1 = (ln(S/K) + (r + σ²/2)·T) / (σ·√T)
d2 = d1 - σ·√T
put = K·e^(-rT)·N(-d2) - S·N(-d1)

Black-Scholes Call Price (via put-call parity)

call = put + S - K·e^(-rT)

Butterfly Put Payoff (expiry)

if S >= K3: 0
if S >= K2: K3 - S
if S >= K1: S - K1
else: 0

Net P&L per share = payoff − premium_paid

Vertical Spread (call debit) Payoff (expiry)

long_call = max(S - K1, 0)
short_call = max(S - K2, 0)
payoff = long_call - short_call - net_debit

Calendar Spread Theoretical Value

Calendar cannot be expressed as a simple expiry function — always use BS pricing for both legs:

value = BS(S, K, T_far, r, IV_far) - BS(S, K, T_near, r, IV_near)

For expiry curve of calendar: near leg expires worthless, far leg = BS with remaining T.

Iron Condor Payoff (expiry)

put_spread = max(K2-S, 0) - max(K1-S, 0)   // short put spread
call_spread = max(S-K3, 0) - max(S-K4, 0)  // short call spread
payoff = credit_received - put_spread - call_spread

Step 4: Render the Widget

Use visualize:read_me with modules ["chart", "interactive"] before building.

Required Controls (sliders)

Structure section:

  • All strike prices (K1, K2, K3... as needed by strategy)
  • Premium paid/received
  • Quantity
  • Multiplier (100 default, show for clarity)

Pricing variables section:

  • IV % (5–80%, step 0.5)
  • DTE — days to expiry (0–90)
  • Risk-free rate % (0–8%)

Spot price:

  • Full-width slider, range = [min_strike - 20%, max_strike + 20%], defaulting to ACTUAL current spot

Required Stats Cards (live-updating)

  • Max profit (expiry)
  • Max loss (expiry)
  • Breakeven(s) — show both for two-sided strategies
  • Current theoretical P&L at spot

Chart Specs

  • X-axis: SPX/underlying price
  • Y-axis: Total USD P&L (not per-share)
  • Blue solid line = theoretical value at current DTE/IV
  • Gray dashed line = expiry payoff
  • Green dashed vertical = strike prices (K2 center strike brighter)
  • Amber dashed vertical = current spot price
  • Fill above zero = green 10% opacity; below zero = red 10% opacity
  • Tooltip: show both curves on hover

Code template

Use this JS structure inside the widget, adapting pnlExpiry() and bfTheory() per strategy:

js
// Black-Scholes helpers (always include)
function normCDF(x) { /* Horner approximation */ }
function bsCall(S,K,T,r,sig) { /* standard BS call */ }
function bsPut(S,K,T,r,sig) { /* standard BS put */ }

// Strategy-specific expiry payoff (returns per-share value BEFORE premium)
function expiryValue(S, ...strikes) { ... }

// Strategy-specific theoretical value using BS
function theoreticalValue(S, ...strikes, T, r, iv) { ... }

// Main update() reads all sliders, computes arrays, destroys+recreates Chart.js instance
function update() { ... }

// Attach listeners
['k1','k2',...,'iv','dte','rate','spot'].forEach(id => {
  document.getElementById(id).addEventListener('input', update);
});
update();

Step 5: Respond to User

After rendering the widget, briefly explain:

  1. What strategy was detected and how legs were mapped
  2. Max profit / max loss at current settings
  3. One key insight (e.g., "spot is currently 950 pts below the profit zone, expiring tomorrow")

Keep it concise — the chart speaks for itself.


Reference Files

  • references/strategies.md — Detailed payoff formulas and edge cases for each strategy type
  • references/bs_code.md — Copy-paste ready Black-Scholes JS implementation with normCDF

Read the relevant reference file if you're unsure about payoff formula edge cases for a given strategy.

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 Options Payoff AI skill do?

Generate an interactive options payoff curve chart with dynamic parameter controls. Use this skill whenever the user shares an options position screenshot, describes an options strategy, or asks to visualize how an options trade makes or loses money. Triggers include: any mention of butterfly, spread (vertical/calendar/diagonal/ratio), straddle, strangle, condor, covered call, protective put, iron condor, or any multi-leg options structure. Also triggers when a user pastes strike prices, premiums, expiry dates, or says things like "show me the payoff", "draw the P&L curve", "what does this...

Why use Options Payoff on TypingMind?

Because you install it once and use it with any model. Options Payoff 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 Options Payoff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/himself65/finance-skills/tree/main/plugins/market-analysis/skills/options-payoff. 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 Options Payoff?

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 Options Payoff?

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

Is the Options Payoff AI skill free?

Yes. It is published on GitHub by himself65 under the MIT 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇