Building a SciPy Optimization Tool for Personal Wealth based on Natal Charts

SciPy-Based Wealth Optimization

Every individual has a unique "Financial Blueprint" encoded in their natal chart. Some are architected for sudden gains, others for slow, steady accumulation. To find your optimal path, we use SciPy's Optimization Engine to solve for the "Maximum Wealth Function" given the constraints of your unique chart.

The Problem: Constrained Optimization

We define a "Wealth Function" based on your chart's strengths (e.g., strong 10th house for career, 11th for networking) and use SciPy to find the optimal allocation of time and capital.

from scipy.optimize import minimize
import numpy as np

# Objective: Maximize Wealth (W)
# Constraints: Time (T), Risk Tolerance (R), Chart Potentials (C)
def objective(x):
    # x[0] = Time in Career, x[1] = Time in Investment, x[2] = Time in Networking
    return -(x[0]*C_career + x[1]*C_invest + x[2]*C_network)

# Constraints: x[0]+x[1]+x[2] <= 24 hours
cons = ({'type': 'eq', 'fun': lambda x:  np.sum(x) - 24})
bnds = ((0, 24), (0, 24), (0, 24))

res = minimize(objective, [8, 8, 8], method='SLSQP', bounds=bnds, constraints=cons)
print(f"Optimal Daily Allocation: {res.x}")

The Result: Your Architected Life

By solving this optimization problem, we move from "struggling against our nature" to "architecting our destiny." We find the path of least resistance to the highest prosperity.