financial analyst with experience in python

Creating a dynamic financial simulation which will have the following main functions:
Main Objectives:
1. Get data through API
2. Index Creation
3. Sharpe ratio
4. Value at Risk (VaR)
5. CAPM and Beta model

1. Get data through API

2. Index creation with following input variables:

1) N = Number of assets = 1 to 150 - user can choose by
i. N – example top20, top50
ii. Add assets in index by symbols – tick boxes from 150 coins that I wish to add

2) X = Rebalancing time interval choose from:
= 1D = daily
= 7D = weekly
= 14D = bi-weekly
= 30D = monthly
= 90D = quarterly
= 182D = semi-annually

3) Strict to flexible rebalancing model - User also needs the option of rebalancing only those assets that has deviated by more than a given percentage from the target allocation
(– for instance – only rebalance those that are +-10% from target allocation, meaning if XRP was supposed to be 2% of portfolio but now is 2.1% of the portfolio, I will not rebalance it but if it was 2.3%, I will) –

3) % Allocation to Strategy
3.1) Market-value Weighted – Asset allocation is equal to market share of individual asset given portfolio
3.2) Equally Weighted – all assets have equal allocation in portfolio

4) Cap (Ceiling) = Max % one asset can take
Option to input cap ceiling percentage that any asset should not exceed

5) Fund Starting Value : $$$
6) Fund Starting Date :

Output:
• Portfolio Value
• Expected Return
• Variance and covariance matrix of index


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Sharpe Ratio:
Return/Risk = Expected value / standard deviation.


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Apply Capitol Asset Pricing Model on daily pricing data from 2013 till Present:

user input: Time frame and candle stick size (1D, 1W, etc)
1. Standard deviation of each asset -
Top 150 assets

2. For every index output, also calculate its variance and covariance

3. Calculate correlation of index with individual assets – user can add symbol of assets for which he wants to see the correlation with or correlation between different indexes.

4. Correlation matrix of all assets –
User can input asset symbols and see the correlation matrix – conditional color formatting of matrix from -1 (dark red) to 1 (dark green)

5. Apply CAPM on market data – additional user input ‘N’.
Calculate Market return – Rm and Beta of each asset from Top 150 and Beta of each index created

6. Theoretical min variance portfolio –user inputs N (for example in top50, top100, top150, ERC20)

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User input: Timeframe
Candle stick size (1D, 1W, etc)
Each asset and each index created

Value at Risk (VaR)
for each asset and index
User input variables: VaR time-frame (month, daily, quarterly)
% of VaR – (5%, 10%)

Graphical illustrations of the output will also be required