https://github.com/ashwinpn/robo-advisor-and-systematic-trading

Basis, execution, and evaluation of execution strategies used in financial markets, and exploring the use of AI in driving decision making.
https://github.com/ashwinpn/robo-advisor-and-systematic-trading

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Basis, execution, and evaluation of execution strategies used in financial markets, and exploring the use of AI in driving decision making.

• Host: GitHub
• URL: https://github.com/ashwinpn/robo-advisor-and-systematic-trading
• Owner: ashwinpn
• Created: 2020-12-19T19:37:01.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-08T15:22:14.000Z (almost 4 years ago)
• Last Synced: 2023-10-19T22:45:53.973Z (about 1 year ago)
• Language: Python
• Size: 1.07 MB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Robo-advisor-and-systematic-trading

Basis, execution, and evaluation of execution strategies used in financial markets, and exploring the use of AI in driving decision making.

In preparation for the Robo advisors and systematic trading course [taught by Prof. Vasant Dhar] at the NYU Stern School of Business.

## Dependencies

Run 

```bash

pip install -r requirements.txt

```

## Portfolio Optimization

- Creating a portfolio with 3 assets from a list of 6 assets [Use of standard tickers used on the NYSE for representing assets].

```python

from roboadvisor.optimizer import PortfolioOptimizer

assets=['TSLA','SPY','GDX','AAPL','FXI','GLD']

optimal_portfolio=PortfolioOptimizer(assets, portfolio_size=5,max_pos=1.0, min_pos=0.0)

```

## Detetcting High Frequency Traders

A kdb+ system can capture and

analyse live data to efficiently track these traders using a handful of common metrics. The metrics serve

two purposes. Firstly, the goal is to identify potential high frequency traders. The aim is then to

determine whether any of the identified traders are negatively impacting the market.

- Order to trade ratio (OTR)

   The order to trade ratio metric calculates the total number of order

  messages divided by the number of trades at a broker, client or account level. It identifies

  traders that are amending or cancelling orders at a far higher rate than they are trading. High

  frequency traders are usually identified to have a ratio greater than 15.

  The goal of the

metric can be tweaked and focused by examining the max OTR in 10-minute buckets or scanning

for bursts in OTR throughout the trading day.

```q

// - Realtime order to trade ratio q code. Calculation based on the last x minutes of the market

OrderToTradeRatio:{[x]

update OrderToTrade:OrderCount%TradeCount from

lj[

select OrderCount:count i by brokerID from dxOrder where

time>.z.P-“u”$x;

(select TradeCount:count i by brokerID:buyBrokerID from

dxTrade where time>.z.P-“u”$x) +

(select TradeCount:count i by brokerID:sellBrokerID from

dxTrade where time>.z.P-“u”$x)

]

// - Select the trade count by broker on the buy and sell side from dxTrade and add the keyed tables together.

// - Count the number of orders from the dxOrder table and left join (lj) the trade count table to create a table comprised of brokerID, OrderCount and TradeCount

```