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README.md

@@ -1,19 +1,20 @@
----
-title: Dynamic Allocation System V1
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# 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.

app.py

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+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.._
+    """)
+

config.py

@@ -0,0 +1,327 @@
+"""
+============================================================
+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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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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requirements.txt

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-altair
-pandas
-streamlit
+streamlit>=1.45
+pandas
+numpy
+plotly
+scikit-learn
+joblib
+stable-baselines3
+gymnasium
+torch
+xgboost
+lightgbm
+yfinance
+matplotlib
+scipy

utils.py

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+# =====================================================
+# 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