utils.py

# =====================================================
# utils.py
# Backend Functions for Portfolio Allocation
# =====================================================

import os
import joblib
import numpy as np
import pandas as pd

from stable_baselines3 import PPO

from config import (
    PPO_PATH,
    RL_FEATURE_DATA,
    PRICE_DATA,
    CORRELATION_MATRIX,
    ALL_STOCKS,
    RISK_MAPPING
)

# =====================================================
# Load PPO Model
# =====================================================

def load_ppo():

    model = PPO.load(PPO_PATH)

    return model

# =====================================================
# Load Deployment Data
# =====================================================

def load_data():

    rl_feature_data = joblib.load(RL_FEATURE_DATA)

    price_data = joblib.load(PRICE_DATA)

    correlation_matrix = joblib.load(CORRELATION_MATRIX)

    return (

        rl_feature_data,

        price_data,

        correlation_matrix

    )

# =====================================================
# Latest Stock Prices
# =====================================================

def get_latest_prices(price_data):

    latest_prices = {}

    for stock in ALL_STOCKS:

        latest_prices[stock] = float(
            price_data[stock].iloc[-1]
        )

    return latest_prices

    

# =====================================================
# Latest Feature Vector
# =====================================================

def get_latest_feature_matrix(

    rl_feature_data

):

    feature_matrix = []

    for stock in ALL_STOCKS:

        latest = (

            rl_feature_data[stock]

            .iloc[-1]

            .values

            .astype(np.float32)

        )

        feature_matrix.extend(latest)

    return np.array(
        feature_matrix,
        dtype=np.float32
    )

def build_observation(



    rl_feature_data,



    budget,



    risk_profile,



    investment_horizon



):

    feature_matrix = get_latest_feature_matrix(
        rl_feature_data
    )

    shares = np.zeros(
        len(ALL_STOCKS),
        dtype=np.float32
    )

    cash = budget

    portfolio_value = budget

    if budget < 100000:

        tier = 0

    elif budget < 500000:

        tier = 1

    else:

        tier = 2

    portfolio_state = np.array(

        [

            budget,

            tier,

            cash,

            RISK_MAPPING[risk_profile],

            investment_horizon,

            portfolio_value

        ],

        dtype=np.float32

    )

    observation = np.concatenate(

        [

            feature_matrix,

            shares,

            portfolio_state

        ]

    )

    return observation

def predict_portfolio(



    model,



    observation



):

    action, _ = model.predict(

        observation,

        deterministic=True

    )

    action = np.clip(

        action,

        0,

        None

    )

    if action.sum() == 0:

        action += 1

    weights = action / action.sum()

    return weights

def adjust_weights_for_risk(weights, risk_profile):
    """

    Post-process PPO allocation according to investor risk.



    PPO decides WHICH stocks are best.

    Risk engine decides HOW MUCH to allocate.



    weights[-1] = cash

    """

    weights = weights.copy()

    stock_weights = weights[:-1]
    cash = weights[-1]

    # Rank stocks according to PPO
    ranked = np.argsort(stock_weights)[::-1]

    # ----------------------------------------------------
    # Conservative
    # ----------------------------------------------------

    if risk_profile == "Conservative":

        cash = 0.15

        template = np.array([
            0.18,
            0.16,
            0.14,
            0.12,
            0.10,
            0.07,
            0.05,
            0.02,
            0.01,
            0.00
        ])

    # ----------------------------------------------------
    # Moderate
    # ----------------------------------------------------

    elif risk_profile == "Moderate":

        return weights

    # ----------------------------------------------------
    # Aggressive
    # ----------------------------------------------------

    else:

        cash = 0.02

        template = np.array([
            0.28,
            0.20,
            0.15,
            0.11,
            0.09,
            0.07,
            0.05,
            0.02,
            0.01,
            0.00
        ])

    # Scale template to remaining capital
    template *= (1 - cash)

    new_stock_weights = np.zeros_like(stock_weights)

    for i, idx in enumerate(ranked):
        new_stock_weights[idx] = template[i]

    final = np.concatenate([new_stock_weights, [cash]])

    return final
    
def generate_portfolio(

    weights,

    budget,

    price_data

):

    latest_prices = get_latest_prices(price_data)

    allocation = []

    cash = budget * weights[-1]

    # Initial purchase
    for stock, weight in zip(ALL_STOCKS, weights[:-1]):

        target = budget * weight

        price = latest_prices[stock]

        shares = int(target // price)

        invested = shares * price

        cash += target - invested

        allocation.append({

            "Stock": stock,
            "Price (₹)": round(price,2),
            "Target": target,
            "Shares": shares,
            "Investment (₹)": invested

        })

    allocation = pd.DataFrame(allocation)

    # ---------------------------------------------------
    # Redistribute remaining cash
    # ---------------------------------------------------

    while True:

        affordable = allocation[
            allocation["Price (₹)"] <= cash
        ]

        if affordable.empty:
            break

        # Remaining amount needed to reach target
        affordable = affordable.copy()

        affordable["Gap"] = (
            affordable["Target"] -
            affordable["Investment (₹)"]
        )

        affordable = affordable.sort_values(
            "Gap",
            ascending=False
        )

        bought = False

        for idx in affordable.index:

            price = allocation.loc[idx, "Price (₹)"]

            if price <= cash:

                allocation.loc[idx, "Shares"] += 1

                allocation.loc[idx, "Investment (₹)"] += price

                cash -= price

                bought = True

                break

        if not bought:
            break

    allocation = allocation[
        allocation["Shares"] > 0
    ]

    allocation["Weight (%)"] = (
        allocation["Investment (₹)"] /
        budget
    ) * 100

    allocation = allocation.drop(
        columns="Target"
    )

    allocation = allocation.sort_values(
        "Investment (₹)",
        ascending=False
    )

    allocation.reset_index(
        drop=True,
        inplace=True
    )

    return allocation, round(cash,2)


# =====================================================
# Main Recommendation Pipeline
# =====================================================

def generate_recommendation(

    budget,

    risk_profile,

    investment_horizon

):

    # Load everything
    model = load_ppo()

    rl_feature_data, price_data, correlation_matrix = load_data()

    snapshot_date = (
    pd.to_datetime(
        next(iter(rl_feature_data.values())).index[-1]
    ).strftime("%d-%b-%Y"))

    # Build PPO observation
    observation = build_observation(
        rl_feature_data=rl_feature_data,
        budget=budget,
        risk_profile=risk_profile,
        investment_horizon=investment_horizon
    )

    # PPO prediction
    weights = predict_portfolio(
        model,
        observation
    )

    weights = adjust_weights_for_risk(
    weights,
    risk_profile
)

    allocation, cash = generate_portfolio(
    weights,
    budget,
    price_data
)

    summary = {

    "Total Investment":
        allocation["Investment (₹)"].sum(),

    "Cash":
        cash,

    "Number of Stocks":
        len(allocation)

}

    return allocation, cash, summary, weights, snapshot_date