Automated summary
Computes iron condor metrics — net credit, max profit and loss, breakevens and profit-zone width — for range-bound credit spread strategies.
Strengths
- Explicit formulas for net credit, max loss (wing width minus credit) and the lower/upper breakeven points.
- Wing-width guidance (narrow, standard, wide) with corresponding risk and credit ranges per contract.
- Concrete criteria for spotting a favorable setup: technical range, IV rank above 50, no major upcoming catalyst.
- Clearly pinned Python dependencies (numpy, pandas, matplotlib, scipy) plus a runnable IronCondor class example.
Limitations
- Spread credits must be supplied by the user; the skill pulls no option chain or live market data.
- It models the position at entry only, with no adjustment, rolling, early close or assignment handling.
- The provided documentation is partial (the wide-wing section is cut off and external references are not included).
- It does not validate input prices or issue explicit loss warnings: this is not financial advice.
Use it when you already have the strikes and credits for an iron condor and want a quick risk/reward check before placing the trade.
Avoid it if you need to scan markets, auto-generate option legs, or receive personalized investment recommendations.
Security analysis
SafeThis skill provides educational content and Python code snippets for options strategy calculations. It does not instruct destructive, exfiltrating, or obfuscated actions, and declares no tools. The code examples are for local numerical analysis using standard libraries, with no network or shell execution risk.
No concerns found
How this analysis is madeExamples
Using the iron-condor skill, compute the net credit, max profit, max loss and breakevens for an iron condor on SPY at 450 with a 440/435 put spread paying 0.75 and a 460/465 call spread paying 0.80, 1 contract.Compare the risk/reward of narrow ($2.50), standard ($5) and wide ($10) wings for an iron condor on a $450 stock, and tell me which one keeps max loss under $500 per contract.I'm looking at a stock consolidating between support at $95 and resistance at $110 with IV rank of 62 and no earnings for six weeks — walk me through whether this is a good iron condor candidate and what strikes to consider.name: iron-condor description: Analyzes iron condor credit spreads with OTM put and call spreads for range-bound trading. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting sideways price action, want to collect premium in high IV, analyzing range-bound opportunities, or implementing neutral income strategies on stocks with defined trading ranges.
Iron Condor Strategy
Version: 1.0 Last Updated: 2025-12-12
Overview
An iron condor is a credit spread strategy that profits from range-bound price action. By selling an OTM put spread and an OTM call spread simultaneously, the trader collects premium upfront and profits when the stock remains between the short strikes through expiration, benefiting from time decay and volatility contraction.
Quick Summary: Sell OTM put spread + Sell OTM call spread = Collect credit, profit from range
Strategy Characteristics
Position Structure:
- Sell 1 OTM put (short put)
- Buy 1 further OTM put (long put, protection)
- Sell 1 OTM call (short call)
- Buy 1 further OTM call (long call, protection)
- All same expiration, typically equal wing widths
Risk Profile:
- Maximum Profit: Net credit received
- Maximum Loss: Wing width - Net credit
- Breakeven Points:
- Lower: Short put - Net credit
- Upper: Short call + Net credit
- Best Use: Expect stock to stay range-bound, benefit from theta and IV crush
Cost Components:
- Put spread credit: Received
- Call spread credit: Received
- Net credit = Total received (keep if stock stays in range)
- Max risk = Wing width - Net credit
- Transaction costs: ~$0.65 per contract × 4 legs = $2.60
Quick Start
Calculate iron condor metrics:
from scripts.iron_condor_calculator import IronCondor
# Example: SPY at $450, expect to stay $440-$460
position = IronCondor(
underlying_price=450.00,
short_put=440.00, # Sell $440 put
long_put=435.00, # Buy $435 put ($5 wide)
short_call=460.00, # Sell $460 call
long_call=465.00, # Buy $465 call ($5 wide)
put_spread_credit=0.75,
call_spread_credit=0.80,
contracts=1
)
# Key metrics
print(f"Net Credit: ${position.net_credit:.2f}")
print(f"Max Profit: ${position.max_profit:.2f}")
print(f"Max Loss: ${position.max_loss:.2f}")
print(f"Breakevens: ${position.lower_breakeven:.2f} - ${position.upper_breakeven:.2f}")
print(f"Profit Zone: {position.profit_zone_width:.0f} points")
Core Workflow
1. Identify Range-Bound Opportunity
Look for stocks likely to trade sideways:
Ideal Scenarios:
- Post-Earnings Consolidation: Stock settles after earnings volatility
- Technical Range: Trading between support/resistance
- High IV Crush Candidate: Elevated IV likely to contract
- Sideways Trend: Lack of directional catalyst
- Index Products: SPY, QQQ during low-volatility periods
Market Conditions:
- IV rank >50 (premium rich environment)
- Recent volatility > Expected future volatility
- Clear support/resistance levels
- No major catalysts upcoming
- Historical range-bound behavior
See references/range-identification.md.
2. Wing Width Selection
Narrow Wings ($2.50-$5):
- Lower max risk ($250-$500 per contract)
- Lower credit collected (~$0.50-$1.50)
- Better risk/reward ratio (often 3:1 or better)
- Easier to manage (less capital at risk)
- Example: $440/$445 put spread + $455/$460 call spread
Standard Wings ($5-$10):
- Moderate max risk ($500-$1,000 per contract)
- Moderate credit (~$1.00-$2.50)
- Balanced approach (most common)
- Example: $435/$445 put spread + $455/$465 call spread
Wide Wings ($10-$20):
- Higher max risk ($1,000-$2,000 per contract)
- Higher credit (~$2.50-$5.00)
- Lower R:R ratio (often <2:1)
- More capital intensive
- Example: $430/$450 put spread + $450/$470 call spread
Selection Criteria:
- Match wing width to risk tolerance
- Wider wings = more credit but worse R:R
- Target R:R ratio: 3:1 or better (credit:risk)
See references/wing-width-selection.md.
3. Strike Placement Framework
Delta-Based Approach (Most Common):
Conservative (Wide profit zone):
- Short put: ~-0.10 delta (10% OTM)
- Short call: ~0.10 delta (10% OTM)
- Probability of profit: ~80%
- Example: Stock $100 → Short $90 put / Short $110 call
Standard (Balanced):
- Short put: ~-0.20 delta (7-8% OTM)
- Short call: ~0.20 delta (7-8% OTM)
- Probability of profit: ~70%
- Example: Stock $100 → Short $92 put / Short $108 call
Aggressive (Narrow profit zone):
- Short put: ~-0.30 delta (5% OTM)
- Short call: ~0.30 delta (5% OTM)
- Probability of profit: ~60%
- Higher credit, tighter range
- Example: Stock $100 → Short $95 put / Short $105 call
See references/strike-placement-guide.md.
4. Profit Zone Optimization
Compare different strike configurations:
from scripts.condor_optimizer import optimize_profit_zone
configs = optimize_profit_zone(
underlying_price=450.00,
short_deltas=[0.10, 0.15, 0.20, 0.25, 0.30],
wing_width=5.00,
volatility=0.22,
days_to_expiration=45
)
for config in configs:
print(f"Delta {config['short_delta']:.2f}:")
print(f" Profit Zone: ${config['lower_short']:.2f} - ${config['upper_short']:.2f}")
print(f" Zone Width: {config['zone_width']:.0f} points ({config['zone_pct']:.1f}%)")
print(f" Credit: ${config['net_credit']:.2f}")
print(f" Max Risk: ${config['max_risk']:.2f}")
print(f" R:R Ratio: {config['rr_ratio']:.2f}:1")
See references/profit-zone-optimization.md.
5. IV Rank and Premium Analysis
Assess volatility environment:
from scripts.iv_analyzer import analyze_iv_for_condor
analysis = analyze_iv_for_condor(
symbol='SPY',
current_iv=0.22,
lookback_period=252
)
print(f"IV Rank: {analysis['iv_rank']:.0f}")
print(f"IV Percentile: {analysis['iv_percentile']:.0f}")
print(f"Premium Rich: {analysis['is_premium_rich']}")
print(f"IV Crush Likely: {analysis['iv_crush_likely']}")
print(f"Optimal for Condor: {analysis['condor_optimal']}")
Ideal IV Environment:
- IV Rank: 50-90 (elevated premiums)
- IV Percentile: 60-90
- Recent IV spike (likely to contract)
- No major catalysts ahead (avoid IV expansion)
⚠️ Avoid when IV rank <30 (insufficient premium)
See references/iv-analysis.md.
6. Expiration Cycle Selection
Short-Term Condors (21-30 days):
- Faster theta decay benefit
- Less time for stock to breach
- More active management required
- Higher annualized return potential
- Best for: Active traders, high-probability setups
Standard Condors (30-45 days):
- Balanced theta and time
- Industry standard (most common)
- Moderate management
- Sweet spot for theta/gamma balance
- Best for: Most traders, most conditions
Long-Term Condors (45-60 days):
- Slower theta decay
- More time for adjustments
- Lower theta benefit
- More forgiving
- Best for: Less active traders, wider ranges
See references/expiration-selection.md.
7. Greeks Monitoring
Track condor Greeks:
from scripts.greeks_calculator import calculate_condor_greeks
greeks = calculate_condor_greeks(
short_put=440.00,
long_put=435.00,
short_call=460.00,
long_call=465.00,
underlying_price=450.00,
volatility=0.22,
time_to_expiration=45/365
)
print(f"Delta: {greeks['delta']:.3f}") # ~0 (neutral)
print(f"Gamma: {greeks['gamma']:.3f}") # Negative (position speeds up outside range)
print(f"Theta: {greeks['theta']:.3f}") # Positive (earns decay)
print(f"Vega: {greeks['vega']:.3f}") # Negative (benefits from IV drop)
Key Insights:
- Delta: Near zero (directionally neutral)
- Gamma: Negative (accelerates against you outside range)
- Theta: Positive (earns time decay daily)
- Vega: Negative (benefits from IV contraction)
See references/greeks-guide.md.
8. Position Sizing
Calculate risk-appropriate contracts:
from scripts/position_sizer import calculate_condor_size
contracts = calculate_condor_size(
portfolio_value=100000,
risk_per_trade=0.02, # 2% max risk
max_loss_per_condor=345 # Wing width $5 - Credit $1.55
)
# Returns: 5 contracts (max risk $1,725 = 1.73%)
Sizing Guidelines:
- Risk 1-3% of portfolio per condor
- Account for max loss (wing width - credit)
- Consider deploying multiple condors at different ranges
- Leave room for adjustments
See references/position-sizing.md.
9. Entry Execution
Order Types:
- Iron Condor Order: Single order for all 4 legs (best execution)
- Credit Limit Order: Set minimum net credit willing to accept
- Separate Spreads: Enter put spread and call spread separately (suboptimal)
Best Practices:
- Always enter as iron condor order (one ticket)
- Set limit at mid-point of condor bid/ask
- Adjust by $0.05-$0.10 if not filled
- Verify equal wing widths before sending
- Confirm credit received (not debit)
Entry Timing:
- Enter when IV rank >50
- After volatility spike (IV likely to contract)
- Avoid day before earnings/events
- Best: 30-45 days to expiration
10. Management and Adjustments
Profit Taking:
- 50% max profit: Standard target (close at 50% of credit)
- 21 DTE: Rolling point (close and re-establish if desired)
- 75% max profit: Near optimal, theta slowing
Defensive Adjustments (Stock approaching short strike):
Roll Untested Side:
- Stock moving up: Close call spread, roll to higher strikes
- Stock moving down: Close put spread, roll to lower strikes
- Keep tested side, collect more credit
Convert to Iron Butterfly:
- Close breached spread
- Sell ATM spread on opposite side
- Tightens profit zone but collects credit
Close and Re-establish:
- Accept loss on current condor
- Open new condor at different range
- Fresh premium, reset probabilities
See references/adjustment-strategies.md.
Scripts
Calculator
# Calculate iron condor metrics
python scripts/iron_condor_calculator.py \
--underlying SPY \
--price 450 \
--short-put 440 \
--long-put 435 \
--short-call 460 \
--long-call 465 \
--put-credit 0.75 \
--call-credit 0.80
Optimizer
# Optimize profit zone configuration
python scripts/condor_optimizer.py \
--underlying SPY \
--price 450 \
--short-deltas 0.10 0.15 0.20 0.25 0.30 \
--wing-width 5 \
--dte 45
IV Analyzer
# Analyze IV environment for condor
python scripts/iv_analyzer.py \
--symbol SPY \
--current-iv 0.22 \
--lookback 252
References
Core Guides
- quickstart-guide.md - 5-minute overview
- installation-guide.md - Setup instructions
- developer-guide.md - Code standards
Strategy-Specific
- range-identification.md - Finding sideways opportunities
- wing-width-selection.md - Choosing spread width
- strike-placement-guide.md - Delta-based framework
- profit-zone-optimization.md - Optimize range
- iv-analysis.md - IV rank, percentile, premium environment
- expiration-selection.md - 21/30/45 day comparison
- greeks-guide.md - Delta, gamma, theta, vega
- position-sizing.md - Risk management
- adjustment-strategies.md - Rolling, defending
- examples.md - Real range-bound setups
Dependencies
Required Packages:
numpy>=1.24.0
pandas>=2.0.0
matplotlib>=3.7.0
scipy>=1.10.0
Installation:
pip install -r requirements.txt
Python Version: 3.11+
Risk Warnings
⚠️ Key Risks:
- Limited Profit: Capped at net credit received
- Undefined Risk (if not managed): Max loss = Wing width - Credit
- Gamma Risk: Position accelerates against you outside range
- Assignment Risk: Short options can be assigned early
- Pin Risk: Stock settling at short strike (complex scenarios)
- Gap Risk: Overnight gaps can breach strikes
- Adjustment Cost: Rolling can be expensive in losses
Risk Mitigation:
- Only trade in high IV environments (IV rank >50)
- Set max loss at 2-3× credit received
- Close early if breached (don't hope)
- Monitor approaching earnings/events
- Use mechanical profit taking (50% rule)
- Size conservatively (1-3% portfolio risk)
- Have adjustment plan before entry
When to Use Iron Condor
✅ Ideal Scenarios:
- Range-bound market (sideways trend)
- High IV environment (IV rank >50)
- Post-earnings consolidation
- No major catalysts upcoming
- Clear support/resistance levels
- Want to collect premium with defined risk
- Comfortable with active management
❌ Avoid When:
- Low IV environment (IV rank <30)
- Trending market (strong directional bias)
- Pre-earnings or major catalyst
- Wide bid/ask spreads (illiquid)
- Can't actively monitor (vacations)
- Uncertain market conditions
Comparison to Other Strategies
vs. Iron Butterfly: Wider profit zone and lower max loss probability, but lower credit.
vs. Long Butterfly: Collect credit vs. pay debit. Benefits from time decay.
vs. Straddle/Strangle: Defined risk with wings. Opposite thesis (want no move vs. big move). Collect premium.
Example Trade
Scenario: SPY at $450, IV rank 72, expect to stay $435-$465 over 45 days
Setup:
- Sell 1 SPY $440 put @ $1.25
- Buy 1 SPY $435 put @ $0.50 (Put spread credit: $0.75)
- Sell 1 SPY $460 call @ $1.30
- Buy 1 SPY $465 call @ $0.50 (Call spread credit: $0.80)
- Net credit: $1.55 × 100 = $155 per condor
- Contracts: 10 condors
- Expiration: 45 days
Risk Profile:
- Max Profit: $155 × 10 = $1,550 (if SPY $440-$460 at expiration)
- Max Loss: ($5 wing - $1.55 credit) × 100 × 10 = $3,450
- Lower Breakeven: $440 - $1.55 = $438.45
- Upper Breakeven: $460 + $1.55 = $461.55
- Profit Zone: $438.45 - $461.55 (23-point range, ±5.1%)
- Risk/Reward: 2.2:1 (excellent)
- Probability of Profit: ~75%
Outcomes: SPY $440-$460 = max profit $1,550; at $461 = breakeven; outside wings = max loss $3,450
Management: Target 50% max profit ($775) or 21 DTE; stop if SPY approaches wings
Version History
v1.0 (2025-12-12)
- Initial release using SKILL_PACKAGE_TEMPLATE v3.0
- Anthropic + Claude Code compliant (<500 lines)
- Progressive disclosure with references/
- Complete condor optimizer and IV analyzer
- Delta-based strike placement framework
- Adjustment strategies and profit zone optimization
Compliance: Anthropic Best Practices ✅ | Claude Code Compatible ✅ Template: SKILL_PACKAGE_TEMPLATE v3.0 Lines: ~490 (under 500-line limit)
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/iron-condor-strategy-mse7iop3' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/iron-condor-strategy-mse7iop3/download' --output '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md.part' && mv '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md.part' '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Original excerpt: Dependencies
Required Packages:
numpy>=1.24.0
pandas>=2.0.0
matplotlib>=3.7.0
scipy>=1.10.0
Installation:
pip install -r requirements.txt
Python Version: 3.11+
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md' && test -s '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/iron-condor' && cp -R '.skill-downloads/iron-condor-strategy-mse7iop3' '.claude/skills/iron-condor'Cursor
test -f '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md' && test -s '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md' && mkdir -p '.cursor/skills' && test ! -e '.cursor/skills/iron-condor' && cp -R '.skill-downloads/iron-condor-strategy-mse7iop3' '.cursor/skills/iron-condor'Codex
test -f '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md' && test -s '.skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md' && mkdir -p '.agents/skills' && test ! -e '.agents/skills/iron-condor' && cp -R '.skill-downloads/iron-condor-strategy-mse7iop3' '.agents/skills/iron-condor'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/iron-condor-strategy-mse7iop3'openskills read 'iron-condor-strategy-mse7iop3'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/iron-condor-strategy-mse7iop3/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
Create Interactive Maps with kepler.gl
Data & AI
Create interactive map visualizations and export standalone HTML using the keplergl Python package. Use to plot geospatial data from DataFrames, GeoJSON, or CSV.
Agent-Driven Parameter Optimization
Data & AI
Guide for optimizing configurable systems with a measurable objective, using bounded trials and evidence-based recommendations.
ICP Visitor Outreach
Data & AI
Identify signed-up users matching the ideal customer profile but stalled, and draft personalized outreach to re-engage them.