Delete DynamicApp

DynamicApp/README.md

@@ -1,20 +0,0 @@
-# Dynamic Allocation System Operational Backtest Freeze File Blueprint
-# Dynamic Portfolio Allocation System
-
-A machine learning based portfolio allocation system using:
-
-- Feature Engineering
-- Gradient Boosting
-- PPO Reinforcement Learning
-
-## Features
-
-- Dynamic portfolio allocation
-- Risk profile selection
-- 5-day and 10-day investment horizon
-- Cash reserve recommendation
-- Equal Weight comparison
-- Portfolio insights
-- Interactive dashboard using Streamlit
-
-Developed as a Final Year Project.

DynamicApp/app.py

@@ -1,338 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-
-from utils import generate_recommendation
-from config import DEFAULT_BUDGET,ALL_STOCKS
-
-# Page Config
-st.set_page_config(
-    page_title="Dynamic Allocation System",
-    page_icon="📈",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom Styling
-st.markdown("""
-<style>
-.main {
-    background-color:#0e1117;
-}
-.block-container {
-    padding-top:2rem;
-}
-div[data-testid="metric-container"] {
-    background:#1b1f2a;
-    border-radius:15px;
-    padding:15px;
-    border:1px solid #2d3748;
-}
-.stButton>button {
-    width:100%;
-    border-radius:10px;
-    height:55px;
-    font-size:18px;
-    font-weight:bold;
-}
-</style>
-""", unsafe_allow_html=True)
-
-# Main Title & Status Badges
-st.title("📈 Dynamic Allocation System")
-
-badge_col1, badge_col2 = st.columns([1, 5])
-with badge_col1:
-    st.success("🟢 Model Status: Loaded")
-with badge_col2:
-    st.info("🤖 Engine: PPO Reinforcement Learning")
-
-st.caption("Machine Learning Based Dynamic Allocation System using Proximal Policy Optimization (PPO)")
-
-
-# Clean fallback safety line
-
-# Sidebar Setup
-st.sidebar.image("Assets/logo.png", use_container_width=True)
-st.sidebar.header("Investment Settings")
-
-budget = st.sidebar.number_input(
-    "Investment Budget (₹)",
-    min_value=10000,
-    max_value=10000000,
-    value=DEFAULT_BUDGET,
-    step=10000
-)
-
-risk = st.sidebar.selectbox(
-    "Risk Profile",
-    ["Conservative", "Moderate", "Aggressive"]
-)
-
-horizon = st.sidebar.radio(
-    "Investment Horizon (Days)",
-    [5, 10]
-)
-
-st.sidebar.divider()
-
-# Updated Model Sidebar (Pipelines and Architecture Overview)
-st.sidebar.info(f"""
-### Model Pipeline
-
-📊 Feature Engineering
-   
-⬇️
-   
-🤖 Gradient Boosting Return Prediction
-   
-⬇️
-   
-🧠 PPO Reinforcement Learning
-   
-⬇️
-   
-💼 Dynamic Portfolio Allocation
-
----
-
-**Universe:** 10 Indian Stocks
-
-**Data Window:** Historical Static Dataset
-
-**Future Enhancement:** Live Market Data Integration
-""")
-
-generate = st.sidebar.button("🚀 Optimize Portfolio", use_container_width=True)
-
-# Main Application Logic
-if generate:
-    with st.spinner("Optimizing Portfolio..."):
-        # Fetch Recommendations
-        allocation, cash, summary, weights, snapshot_date  = generate_recommendation(budget, risk, horizon)
-        
-        # Data Cleaning, Sorting & Precision Preprocessing
-        allocation = allocation.sort_values("Weight (%)", ascending=False).reset_index(drop=True)
-        allocation["Investment (₹)"] = allocation["Investment (₹)"].round(0)
-        allocation["Weight (%)"] = allocation["Weight (%)"].round(2)
-        
-        # Asset Allocation Descriptive Labels
-        allocation["Recommendation"] = allocation["Weight (%)"].apply(
-            lambda x: "🟢 High Allocation" if x >= 15 else ("🟡 Medium Allocation" if x >= 7 else "⚪ Low Allocation")
-        )
-        
-        # 1. Adaptable Universe Scaling: Calculate purely from returned dataset dimensions
-        total_universe = len(allocation)
-        allocated_count = (allocation["Weight (%)"] > 0.5).sum()
-        
-        # KPI Metric Cards
-        col1, col2, col3, col4 = st.columns(4)
-        col1.metric("💰 Investment Budget", f"₹{budget:,.0f}")
-        col2.metric("📈 Recommended Investment", f"₹{summary['Total Investment']:,.0f}")
-        col3.metric("💵 Cash Reserve", f"₹{cash:,.0f}")
-        col4.metric("📊 Stocks Allocated", f"{allocated_count} / {total_universe}")
-        
-        # Split Layout: Pie Chart and Recommendation Summary Panels
-        left, right = st.columns([3, 2])
-        
-        with left:
-            st.subheader("Recommended Portfolio Allocation")
-            fig = px.pie(
-                allocation,
-                names="Stock",
-                values="Investment (₹)",
-                hole=0.45
-            )
-            fig.update_layout(
-                height=550,
-                legend_title="Stocks"
-            )
-            st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})
-            
-        with right:
-            st.subheader("Recommendation Summary")
-            st.success(f"💵 Cash Reserve\n\n₹{cash:,.0f}")
-            
-            diversification = (allocation["Weight (%)"] > 5).sum() / total_universe
-            st.write("Diversification Index")
-            st.progress(diversification)
-            
-            # Additional User Context Metrics
-            meta_col1, meta_col2 = st.columns(2)
-            with meta_col1:
-                st.markdown(f"**Recommendation Based On:** {snapshot_date}")
-            with meta_col2:
-                st.markdown(f"**Investment Horizon:** {horizon} Days")
-
-        # Horizontal Bar Chart (Sorted)
-        st.divider()
-        st.subheader("📊 Stock Allocation Balance")
-        
-        bar_fig = px.bar(
-            allocation,
-            x="Weight (%)",
-            y="Stock",
-            orientation="h",
-            text="Weight (%)"
-        )
-        bar_fig.update_traces(
-            texttemplate="%{text:.1f} %",
-            textposition="outside"
-        )
-        bar_fig.update_layout(
-            yaxis=dict(categoryorder="total ascending"),
-            height=500,
-            xaxis_title="Portfolio Weight (%)",
-            yaxis_title=""
-        )
-        st.plotly_chart(bar_fig, use_container_width=True, config={"displayModeBar": False})
-
-        # PPO vs Equal Weight Comparison Strategy Chart
-        st.divider()
-        st.subheader("⚖ PPO vs Equal Weight Comparison")
-        
-        comparison = allocation.copy()
-        comparison["Equal Weight"] = 100 / total_universe
-        comparison = comparison.rename(columns={"Weight (%)": "PPO"})
-        
-        comparison_long = comparison.melt(
-            id_vars="Stock",
-            value_vars=["PPO", "Equal Weight"],
-            var_name="Strategy",
-            value_name="Weight"
-        )
-        
-        comp_fig = px.bar(
-            comparison_long,
-            x="Stock",
-            y="Weight",
-            color="Strategy",
-            barmode="group",
-            text="Weight"
-        )
-        comp_fig.update_traces(
-            texttemplate="%{text:.1f}%",
-            textposition="outside"
-        )
-        st.plotly_chart(comp_fig, use_container_width=True, config={"displayModeBar": False})
-        st.caption("_Equal Weight assigns an identical fixed allocation to every stock, while the PPO Reinforcement Learning agent dynamically adjusts allocations based on learned market risk patterns and current portfolio states._")
-        
-        # Structural Asset Allocation Statistics
-        st.divider()
-        c1, c2, c3 = st.columns(3)
-        c1.metric("Largest Allocation", f"{allocation.iloc[0]['Weight (%)']:.1f}%")
-        c2.metric("Average Allocation", f"{allocation['Weight (%)'].mean():.1f}%")
-        c3.metric("Diversified Stocks (>5% Weight)", (allocation["Weight (%)"] > 5).sum())
-
-        # Cleaned Standard Header Title "Recommended Portfolio"
-        st.divider()
-        st.subheader("📋 Recommended Portfolio")
-        
-        table_display = allocation[["Stock", "Weight (%)", "Investment (₹)", "Recommendation"]].copy()
-        cash_weight = (cash / budget) * 100
-        cash_row = pd.DataFrame([{
-            "Stock": "💵 Cash Reserve",
-            "Weight (%)": round(cash_weight, 2),
-            "Investment (₹)": round(cash, 0),
-            "Recommendation": "Liquidity Reserve"
-        }])
-        table_display = pd.concat([table_display, cash_row], ignore_index=True)
-        
-        st.dataframe(
-            table_display,
-            use_container_width=True,
-            hide_index=True
-        )
-
-        # Highlight Top Asset Pick
-        st.divider()
-        top = allocation.iloc[0]
-        st.success(f"""
-        ## ⭐ Top Recommendation
-        ### {top['Stock']}
-        
-        Recommended Investment
-        ### ₹{top['Investment (₹)']:,.0f}
-        
-        Portfolio Weight
-        ### {top['Weight (%)']:.2f}%
-        
-        Allocation Strategy
-        ### {top['Recommendation']}
-        """)
-
-        # Dynamic Portfolio Insights Panel
-        st.divider()
-        st.subheader("💡 Dynamic Portfolio Insights")
-        
-        diversified_count = (allocation["Weight (%)"] > 5).sum()
-        overweighted_df = allocation[allocation["Weight (%)"] > (100 / total_universe)]
-        overweighted_names = overweighted_df["Stock"].head(3).tolist()
-        overweighted_str = ", ".join(overweighted_names) if overweighted_names else "None"
-        
-        st.write(f"""
-        * 📈 **Highest Allocation Asset:** **{top['Stock']}** received the highest portfolio allocation from the PPO agent at **{top['Weight (%)']:.2f}%**.
-        * 📉 **Lowest Allocation Asset:** **{allocation.iloc[-1]['Stock']}** has the lowest framework allocation at **{allocation.iloc[-1]['Weight (%)']:.2f}%**.
-        * 💵 **Liquidity Management:** Cash balance retention is securely held at **₹{cash:,.0f}** as a tactical Liquidity Reserve.
-        * 🧩 **Diversification Scope:** **{diversified_count} out of {total_universe}** asset blocks successfully crossed the 5% concentration limit index.
-        * 🧠 **RL Comparison Profile:** Compared with an equal-weight strategy, the PPO agent allocated larger weights to: **{overweighted_str}**.
-        """)
-
-        # Capital Utilization Gauge Graphic
-        st.divider()
-        st.subheader("💰 Capital Utilization")
-        
-        invested = summary["Total Investment"]
-        utilization_percent = (invested / budget) * 100
-        
-        gauge_fig = go.Figure(
-            go.Indicator(
-                mode="gauge+number",
-                value=utilization_percent,
-                number={"suffix": "%"},
-                title={"text": f"₹{invested:,.0f} / ₹{budget:,.0f}"},
-                gauge={
-                    "axis": {"range": [0, 100]},
-                    "bar": {"color": "#10B981"}
-                }
-            )
-        )
-        gauge_fig.update_layout(height=350)
-        st.plotly_chart(gauge_fig, use_container_width=True, config={"displayModeBar": False})
-
-        # Natural Download Branding Action String
-        st.divider()
-        csv = table_display.to_csv(index=False)
-        st.download_button(
-            label="📥 Download Recommendation",
-            data=csv,
-            file_name="Dynamic_Allocation_Recommendation.csv",
-            mime="text/csv",
-            use_container_width=True
-        )
-
-else:
-    # Humanized Welcome Screen Layout
-    st.info("""
-    👋 **Welcome to the Dynamic Allocation System.** Configure your investment preferences using the sidebar and click **"Optimize Portfolio"** to receive a personalized portfolio allocation generated by the PPO reinforcement learning agent.
-    """)
-
-# Polished Software About Layout View
-st.markdown("<br>", unsafe_allow_html=True)
-with st.expander("ℹ️ About This Project"):
-    st.write(f"""
-    This application demonstrates a machine learning-based dynamic portfolio allocation system.
-    
-    The pipeline consists of:
-    * **Feature Engineering Framework:** Extracts trend, momentum, and risk indicators.
-    * **Gradient Boosting Prediction Engine:** Estimates expected asset trajectory behaviors.
-    * **PPO Reinforcement Learning Strategy:** Evaluates continuous state transitions to optimize portfolio allocations.
-    
-    ---
-    * **Deployment Status:** Historical Static Dataset for Reproducibility
-    * **System Architecture:** Pipeline Integration v1.0
-    
-    _Future versions will support live market data streaming and programmatic broker APIs. Recommendations are generated from the historical dataset.._
-    """)
-

DynamicApp/config.py

@@ -1,327 +0,0 @@
-"""
-============================================================
-AI Dynamic Portfolio Management System
-Project Configuration
-============================================================
-
-Author  : Vansh Putalu
-Version : 2.0
-Purpose : Global configuration shared across all notebooks
-"""
-
-import os
-
-# ============================================================
-# PROJECT PATHS
-# ============================================================
-
-PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
-
-DATA_PATH = os.path.join(PROJECT_ROOT, "data")
-
-RAW_PATH = os.path.join(DATA_PATH, "raw")
-
-PROCESSED_PATH = os.path.join(DATA_PATH, "processed")
-
-FEATURE_PATH = os.path.join(DATA_PATH, "features")
-
-MODEL_PATH = os.path.join(PROJECT_ROOT, "models")
-
-RESULT_PATH = os.path.join(PROJECT_ROOT, "results")
-
-IMAGE_PATH = os.path.join(RESULT_PATH, "figures")
-
-REPORT_PATH = os.path.join(PROJECT_ROOT, "reports")
-
-# ============================================================
-# RAW DATA FOLDERS
-# ============================================================
-
-STOCK_PATH = os.path.join(RAW_PATH, "stocks")
-
-MARKET_PATH = os.path.join(RAW_PATH, "market")
-
-MACRO_PATH = os.path.join(RAW_PATH, "macro")
-
-SECTOR_PATH = os.path.join(RAW_PATH, "sector")
-
-METADATA_PATH = os.path.join(RAW_PATH, "metadata")
-
-# ============================================================
-# CREATE DIRECTORIES
-# ============================================================
-
-DIRECTORIES = [
-
-    DATA_PATH,
-
-    RAW_PATH,
-
-    PROCESSED_PATH,
-
-    FEATURE_PATH,
-
-    MODEL_PATH,
-
-    RESULT_PATH,
-
-    IMAGE_PATH,
-
-    REPORT_PATH,
-
-    STOCK_PATH,
-
-    MARKET_PATH,
-
-    MACRO_PATH,
-
-    SECTOR_PATH,
-
-    METADATA_PATH
-
-]
-
-for directory in DIRECTORIES:
-
-    os.makedirs(directory, exist_ok=True)
-
-# ============================================================
-# DOWNLOAD PERIOD
-# ============================================================
-
-START_DATE = "2008-01-01"
-
-END_DATE = "2026-12-31"
-
-# ============================================================
-# PROJECT SETTINGS
-# ============================================================
-
-RANDOM_STATE = 42
-
-TRAIN_RATIO = 0.80
-
-TRADING_DAYS = 252
-
-TARGET_HORIZONS = [5, 10]
-
-DEFAULT_TARGET = "Target_5D"
-
-TARGET_TYPE = "Regression"
-
-# ============================================================
-# PORTFOLIO SETTINGS
-# ============================================================
-
-INITIAL_CAPITAL = 100000
-
-MONTHLY_SIP = 10000
-
-REBALANCE_FREQUENCY = 5
-
-MAX_STOCK_WEIGHT = 0.30
-
-MIN_STOCK_WEIGHT = 0.00
-
-# ============================================================
-# STOCKS
-# ============================================================
-
-ASSETS = {
-
-    "TCS":{
-
-        "ticker":"TCS.NS",
-
-        "sector":"IT"
-
-    },
-
-    "Infosys":{
-
-        "ticker":"INFY.NS",
-
-        "sector":"IT"
-
-    },
-
-    "HDFC_Bank":{
-
-        "ticker":"HDFCBANK.NS",
-
-        "sector":"Banking"
-
-    },
-
-    "ICICI_Bank":{
-
-        "ticker":"ICICIBANK.NS",
-
-        "sector":"Banking"
-
-    },
-
-    "Reliance":{
-
-        "ticker":"RELIANCE.NS",
-
-        "sector":"Energy"
-
-    },
-
-    "ONGC":{
-
-        "ticker":"ONGC.NS",
-
-        "sector":"Energy"
-
-    },
-
-    "ITC":{
-
-        "ticker":"ITC.NS",
-
-        "sector":"FMCG"
-
-    },
-
-    "Hindustan_Unilever":{
-
-        "ticker":"HINDUNILVR.NS",
-
-        "sector":"FMCG"
-
-    },
-
-    "Sun_Pharma":{
-
-        "ticker":"SUNPHARMA.NS",
-
-        "sector":"Pharma"
-
-    },
-
-    "Cipla":{
-
-        "ticker":"CIPLA.NS",
-
-        "sector":"Pharma"
-
-    }
-
-}
-
-ALL_STOCKS = list(ASSETS.keys())
-
-# ============================================================
-# INDIAN MARKET
-# ============================================================
-
-MARKET_INDICES = {
-
-    "NIFTY50":"^NSEI",
-
-    "BANKNIFTY":"^NSEBANK",
-
-    "INDIA_VIX":"^INDIAVIX"
-
-}
-
-# ============================================================
-# GLOBAL MACRO
-# ============================================================
-
-GLOBAL_MACRO = {
-
-    "NASDAQ":"^IXIC",
-
-    "SP500":"^GSPC",
-
-    "DOWJONES":"^DJI",
-
-    "USDINR":"INR=X",
-
-    "DXY":"DX-Y.NYB",
-
-    "Crude_Oil":"CL=F",
-
-    "Gold":"GC=F",
-
-    "Silver":"SI=F",
-
-    "Copper":"HG=F"
-
-}
-
-# ============================================================
-# SECTOR INDICES
-# ============================================================
-
-SECTOR_INDICES = {
-
-    "NIFTY_IT":"^CNXIT",
-
-    "NIFTY_PHARMA":"^CNXPHARMA",
-
-    "NIFTY_FMCG":"^CNXFMCG",
-
-    "NIFTY_ENERGY":"^CNXENERGY",
-
-    "NIFTY_BANK":"^NSEBANK"
-
-}
-
-# ============================================================
-# PROJECT SUMMARY
-# ============================================================
-
-TOTAL_STOCKS = len(ASSETS)
-
-TOTAL_MARKET = len(MARKET_INDICES)
-
-TOTAL_MACRO = len(GLOBAL_MACRO)
-
-TOTAL_SECTOR = len(SECTOR_INDICES)
-
-TOTAL_DOWNLOADS = (
-
-    TOTAL_STOCKS +
-
-    TOTAL_MARKET +
-
-    TOTAL_MACRO +
-
-    TOTAL_SECTOR
-
-)
-
-RISK_MAPPING = {
-
-    "Conservative": 0,
-    "Moderate": 1,
-    "Aggressive": 2
-
-}
-
-HORIZON_MAPPING = {
-
-    "5 Days": 5,
-    "10 Days": 10
-
-}
-
-MODEL_FOLDER = "Models"
-
-PPO_PATH = "Models/ppo_dynamic_allocator_final300"
-
-MODEL_5D = "Models/TargetReturn_5D"
-
-MODEL_10D = "Models/TargetReturn_10D"
-
-PRICE_DATA = "Models/price_data.pkl"
-
-RL_FEATURE_DATA = "Models/rl_feature_data.pkl"
-
-CORRELATION_MATRIX = "Models/correlation_matrix.pkl"
-
-DEFAULT_BUDGET = 500000

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DynamicApp/models/Target_Return_5D/HDFC_Bank.pkl

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DynamicApp/models/Target_Return_5D/ITC.pkl

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DynamicApp/models/regime_map.json

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-{
-    "0": "Bull Market",
-    "1": "Normal Market",
-    "2": "Global Correction",
-    "3": "Global Recovery",
-    "4": "Bear Market",
-    "5": "Strong Bull Rally"
-}

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DynamicApp/requirements.txt

@@ -1,14 +0,0 @@
-streamlit>=1.45
-pandas
-numpy
-plotly
-scikit-learn
-joblib
-stable-baselines3
-gymnasium
-torch
-xgboost
-lightgbm
-yfinance
-matplotlib
-scipy

DynamicApp/utils.py

@@ -1,334 +0,0 @@
-# =====================================================
-# 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 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 generate_portfolio(
-
-    weights,
-
-    budget
-
-):
-
-    allocation = pd.DataFrame({
-
-        "Stock": ALL_STOCKS,
-
-        "Weight (%)": weights[:-1] * 100,
-
-        "Investment (₹)": weights[:-1] * budget
-
-    })
-
-    allocation = allocation.sort_values(
-
-        "Weight (%)",
-
-        ascending=False
-
-    )
-
-    allocation.reset_index(
-
-        drop=True,
-
-        inplace=True
-
-    )
-
-    return allocation
-
-def cash_remaining(
-
-    weights,
-
-    budget
-
-):
-
-    return weights[-1] * budget
-
-def portfolio_summary(
-
-    weights,
-
-    budget
-
-):
-
-    return {
-
-        "Total Investment":
-
-            budget -
-
-            cash_remaining(
-
-                weights,
-
-                budget
-
-            ),
-
-        "Cash":
-
-            cash_remaining(
-
-                weights,
-
-                budget
-
-            ),
-
-        "Number of Stocks":
-
-            np.sum(
-
-                weights[:-1] > 0
-
-            )
-
-    }
-
-# =====================================================
-# 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
-    )
-
-    # Portfolio table
-    allocation = generate_portfolio(
-        weights,
-        budget
-    )
-
-    # Cash
-    cash = cash_remaining(
-        weights,
-        budget
-    )
-
-    # Summary
-    summary = portfolio_summary(
-        weights,
-        budget
-    )
-
-    return allocation, cash, summary, weights, snapshot_date