modelscope.cn
Analyzes protective collar strategies combining long stock, long put protection, and short call income.
Related neighbors and high-traction skills in the same topics — useful to compare before installing.
Guidance for distinctive, intentional visual design when building new UI or reshaping an existi…
866.4K installsBrowser automation CLI for AI agents. Use when the user needs to interact with websites, includ…
810.4K installsReview UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "chec…
617.3K installsBuild, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and …
576.5K installsDebug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe …
568.9K installsOther skills from modelscope.cn · top by installs.
npx skills add https://modelscope.cn
Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.
Parsed from SKILL.md frontmatter.
Files included with this skill beyond the listing page.
SKILL.md
12,642 B
Version: 1.0 Last Updated: 2025-12-12
A protective collar combines stock ownership with options to create a defined-risk position. By purchasing a protective put (downside insurance) and selling a covered call (upside income), the strategy limits both loss and gain while typically reducing or eliminating the net cost of protection.
Quick Summary: Own stock + Buy OTM put + Sell OTM call = Protected position with capped upside
Position Structure:
Risk Profile:
Cost Components:
Calculate protective collar metrics:
from scripts.protective_collar_calculator import ProtectiveCollar
# Example: Protect 100 shares of AAPL at $175
position = ProtectiveCollar(
stock_price=175.00,
shares=100,
put_strike=165.00, # 5.7% OTM protection
put_premium=3.50,
call_strike=185.00, # 5.7% OTM cap
call_premium=3.25
)
# Key metrics
print(f"Net Cost: ${position.net_premium:.2f}")
print(f"Max Loss: ${position.max_loss:.2f}")
print(f"Max Profit: ${position.max_profit:.2f}")
print(f"Protected Range: ${position.put_strike}-${position.call_strike}")Identify stock position requiring protection:
Ideal Candidates:
Criteria:
Choose protective put strike based on risk tolerance:
Conservative (5-10% OTM):
Moderate (10-15% OTM):
Aggressive (15-20% OTM):
See [references/strike-selection-guide.md](references/strike-selection-guide.md).
Choose covered call strike based on upside willingness:
Conservative (10-15% OTM):
Moderate (5-10% OTM):
Aggressive (ATM to 5% OTM):
Compare different collar configurations:
from scripts.collar_analyzer import analyze_collar_configurations
configs = analyze_collar_configurations(
stock_price=175.00,
shares=100,
put_otm_percent=[5, 10, 15], # Put 5%, 10%, 15% OTM
call_otm_percent=[5, 10, 15], # Call 5%, 10%, 15% OTM
volatility=0.25,
days_to_expiration=60
)
for config in configs:
print(f"Put ${config['put_strike']} / Call ${config['call_strike']}")
print(f" Net Cost: ${config['net_premium']:.2f}")
print(f" Protected Range: {config['protection_width']:.1f}%")See [references/collar-configurations.md](references/collar-configurations.md).
Adjust strikes to achieve zero net premium:
from scripts.zero_cost_optimizer import find_zero_cost_collar
result = find_zero_cost_collar(
stock_price=175.00,
target_put_delta=-0.25, # Desired protection level
volatility=0.25,
days_to_expiration=60
)
print(f"Put Strike: ${result['put_strike']:.2f}")
print(f"Call Strike: ${result['call_strike']:.2f}")
print(f"Net Cost: ${result['net_cost']:.2f}") # ~$0Trade-off: Zero-cost collar typically requires wider strikes (less protection, more upside cap).
See [references/zero-cost-optimization.md](references/zero-cost-optimization.md).
Standard Cycles:
Considerations:
See [references/expiration-analysis.md](references/expiration-analysis.md).
Monitor collar Greeks:
from scripts.greeks_calculator import calculate_collar_greeks
greeks = calculate_collar_greeks(
stock_price=175.00,
put_strike=165.00,
call_strike=185.00,
volatility=0.25,
time_to_expiration=60/365
)
print(f"Delta: {greeks['delta']:.3f}") # ~1.0 (stock-like)
print(f"Theta: {greeks['theta']:.3f}") # Minimal (offset legs)
print(f"Vega: {greeks['vega']:.3f}") # Near zero (offsetting)See [references/greeks-guide.md](references/greeks-guide.md).
Order Sequence:
Best Practices:
At Expiration:
Scenario 1: Stock between strikes (most common):
Scenario 2: Stock below put strike:
Scenario 3: Stock above call strike:
Early Management:
See [references/management-strategies.md](references/management-strategies.md).
⚠️ Important Tax Implications:
Qualified Covered Call:
Unqualified Covered Call:
See [references/tax-considerations.md](references/tax-considerations.md).
# Calculate protective collar metrics
python scripts/protective_collar_calculator.py \
--stock AAPL \
--price 175 \
--shares 100 \
--put-strike 165 \
--put-premium 3.50 \
--call-strike 185 \
--call-premium 3.25# Compare collar configurations
python scripts/collar_analyzer.py \
--stock AAPL \
--price 175 \
--put-otm 5 10 15 \
--call-otm 5 10 15 \
--dte 60# Find zero-cost collar strikes
python scripts/zero_cost_optimizer.py \
--stock AAPL \
--price 175 \
--put-delta -0.25 \
--dte 60Required Packages:
numpy>=1.24.0
pandas>=2.0.0
matplotlib>=3.7.0
scipy>=1.10.0Installation:
pip install -r requirements.txtPython Version: 3.11+
⚠️ Key Risks:
Risk Mitigation:
✅ Ideal Scenarios:
❌ Avoid When:
vs. Married Put:
vs. Covered Call:
vs. No Hedge:
Scenario: Own 200 shares MSFT at $350, protect through earnings
Setup:
Risk Profile:
Outcomes:
Compliance: Anthropic Best Practices ✅ | Claude Code Compatible ✅ Template: SKILLPACKAGETEMPLATE v3.0 Lines: ~470 (under 500-line limit)