Hugging Face's logo

Spaces:
bhanuprasanna527
/
CapiPort
Running

App Files Community
CapiPort
File size: 2,792 Bytes 
cb96d56
0c2b3a8
eaf5737
0c2b3a8
 
 
 
 
 
 
9f6b7ef
a85dbe3
531104f
9f6b7ef
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
454a16a
 
 
d8ae909
0607ae0
d5de817
 
 
d8ae909
454a16a
 
9f6b7ef
d8ae909
9f6b7ef
d8ae909
9f6b7ef
89761c8
9f6b7ef
 
 
89761c8
9f6b7ef
89761c8
9f6b7ef
 
89761c8
9f6b7ef
 
 
 
 
29f11f8
 
8ad42cf
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
 
---
title: CapiPort
emoji: 📈
sdk: streamlit
sdk_version: 1.32.0
app_file: main.py
pinned: false
license: mit
---

# <center>CapiPort V2</center>
[![Build Status](https://github.com/bhanuprasanna527/CapiPortV2/actions/workflows/HF_sync_space.yml/badge.svg)](https://github.com/bhanuprasanna527/CapiPortV2/actions)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/bhanuprasanna527/CapiPortV2/blob/main/LICENSE)

## Overview

Welcome to our project on portfolio management for Indian equity markets! This project aims to help individuals efficiently allocate their money between different equities, optimizing returns while managing risk.

## Features

- **Dynamic Allocation:** Our technique dynamically allocates funds among various equities based on a robust methodology.
- **Risk Management:** The project incorporates risk management strategies to enhance overall portfolio stability.
- **User-Friendly Interface:** Access the tool through our user-friendly web interface [here](https://huggingface.co/spaces/bhanuprasanna527/CapiPort).

## Getting Started

Follow these steps to get started with the project:

1. Clone the repository:

   ```bash
   git clone https://github.com/bhanuprasanna527/CapiPort/

2. Install dependencies:
   ```bash
    pip install -r requirements.txt

3. Run the project:
   ```bash
    python main.py

## Technique used (Version 2) 

1) Efficient Frontier
   - Parameters used:
     
        1.1) Maximum Sharpe Ratio\
        1.2) Efficient Risk\
        1.3) Efficient Return\
        1.4) Minimum Volatility
     
2) Hierarchical Risk Parity

# Overview

1. Implementation

   Input: NSE Stock Tickers, Date you want to track from, Optimization Technique, Parameter to base on, and the amount you want to invest.

   Objective: Maximize expected return while minimizing portfolio variance.

   Optimization: Utilize an Optimization method to find the Optimal Allocation to maximize Returns and minimize Volatility. approach, adjusting weights to find the optimal allocation.

   Output: The final set of weights representing the optimal portfolio allocation, Annual Returns, Cumulative Annual Returns, Cumulative Monthly Returns, and Monthly returns. You will also be able to see Visual charts for the analysis.

#### Contributing
We welcome contributions! If you have any improvement ideas, please feel free to open an issue or submit a pull request.
License

This project is licensed under the MIT License.

## Links
1. **[Streamlit Deployment](https://capiport2.streamlit.app/)**
2. **[HuggingFace Spaces](https://huggingface.co/spaces/bhanuprasanna527/CapiPort)**
3. **[Version1 github](https://github.com/bhanuprasanna527/CapiPort)**
4. **[Version1 Spaces](https://huggingface.co/spaces/sankhyikii/CapiPort)**