Sensitivity Analysis

from sensitivity import run_sensitivity_analysis
from models import SimulationRequest, FinancialProfile, UserInputs, Goal

request = SimulationRequest(
    financial_profile=FinancialProfile(
        liquid_assets=10000,
        monthly_spending=4000
    ),
    user_inputs=UserInputs(
        monthly_income=5000,
        age=30,
        risk_tolerance="medium"
    ),
    goal=Goal(
        target_amount=50000,
        timeline_months=48
    )
)

analysis = run_sensitivity_analysis(request)
print(f"Base probability: {analysis.base_probability:.1%}")
print(f"Most impactful change: {analysis.most_impactful}")
for rec in analysis.recommendations:
    print(f"- {rec}")

analysis = run_sensitivity_analysis(request)

print(f"Base success rate: {analysis.base_probability:.1%}")
# Base success rate: 68.4%

print(f"\nMost impactful: {analysis.most_impactful}")
# Most impactful: spending_minus_20

print("\nAll scenarios:")
for name, result in analysis.sensitivities.items():
    print(f"{name:20} -> {result.new_probability:.1%} (Δ {result.delta:+.1%})")

# Output:
# income_plus_10       -> 74.2% (Δ +5.8%)
# income_minus_10      -> 62.1% (Δ -6.3%)
# spending_minus_10    -> 79.5% (Δ +11.1%)
# spending_minus_20    -> 88.3% (Δ +19.9%)
# spending_plus_10     -> 58.7% (Δ -9.7%)
# timeline_plus_6mo    -> 75.6% (Δ +7.2%)
# timeline_plus_12mo   -> 81.4% (Δ +13.0%)

print("\nRecommendations:")
for rec in analysis.recommendations:
    print(f"- {rec}")

# Output:
# - Reducing spending by 10% could improve your success probability by 11% (to 80%).
# - Extending your timeline by 6 months improves probability to 76%.
# - You're on track! Your current plan has strong odds of success. Stay consistent with your savings.

Reducing spending by 10% could improve your success probability by 11% (to 80%).

Increasing income by 10% (raise, side gig) could boost your odds by 8%.

Extending your timeline by 6 months improves probability to 76%.

Your current plan has less than 50% success probability. Consider adjusting your goal, timeline, or savings rate.

You're on track! Your current plan has strong odds of success. Stay consistent with your savings.

# Current job
base_request = SimulationRequest(...)
base_analysis = run_sensitivity_analysis(base_request)

# New job offer with 15% higher salary
import copy
new_job_request = copy.deepcopy(base_request)
new_job_request.user_inputs.monthly_income *= 1.15
new_job_analysis = run_sensitivity_analysis(new_job_request)

print(f"Current job success rate: {base_analysis.base_probability:.1%}")
print(f"New job success rate: {new_job_analysis.base_probability:.1%}")
print(f"Improvement: {new_job_analysis.base_probability - base_analysis.base_probability:.1%}")

analysis = run_sensitivity_analysis(request)

# Find the most efficient spending cut
spending_scenarios = {
    k: v for k, v in analysis.sensitivities.items() 
    if k.startswith('spending_minus')
}

best = max(spending_scenarios.items(), key=lambda x: x[1].impact)
print(f"Best spending strategy: {best[0]}")
print(f"Improves probability to {best[1].new_probability:.1%}")

analysis = run_sensitivity_analysis(request)

timeline_6mo = analysis.sensitivities['timeline_plus_6mo']
timeline_12mo = analysis.sensitivities['timeline_plus_12mo']

print(f"Original timeline: {analysis.base_probability:.1%}")
print(f"6 months longer: {timeline_6mo.new_probability:.1%} (+{timeline_6mo.delta:.1%})")
print(f"12 months longer: {timeline_12mo.new_probability:.1%} (+{timeline_12mo.delta:.1%})")

if timeline_6mo.new_probability > 0.80 and analysis.base_probability < 0.80:
    print("Extending by 6 months gets you to 80% success rate!")

from monte_carlo import run_monte_carlo
import copy

# Test 30% spending reduction
custom_request = copy.deepcopy(request)
custom_request.financial_profile.monthly_spending *= 0.70

base_results = run_monte_carlo(request, n_workers=2)
custom_results = run_monte_carlo(custom_request, n_workers=2)

impact = custom_results.success_probability - base_results.success_probability
print(f"30% spending cut impact: {impact:+.1%}")