https://github.com/mrtkp9993/quantitavefinanceexamplespy

Financial analysis, algorithmic trading, portfolio optimization examples with Python (DISCLAIMER - No Investment Advice Provided, YASAL UYARI - Yatırım tavsiyesi değildir).
https://github.com/mrtkp9993/quantitavefinanceexamplespy

market-data portfolio-optimization python quant quantitative-finance quantitative-trading stock-analysis stock-market

Last synced: 2 months ago
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Financial analysis, algorithmic trading, portfolio optimization examples with Python (DISCLAIMER - No Investment Advice Provided, YASAL UYARI - Yatırım tavsiyesi değildir).

• Host: GitHub
• URL: https://github.com/mrtkp9993/quantitavefinanceexamplespy
• Owner: mrtkp9993
• License: gpl-3.0
• Created: 2021-08-29T11:51:38.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-11-25T15:14:02.000Z (almost 3 years ago)
• Last Synced: 2025-05-07T16:45:00.609Z (5 months ago)
• Topics: market-data, portfolio-optimization, python, quant, quantitative-finance, quantitative-trading, stock-analysis, stock-market
• Language: Jupyter Notebook
• Homepage:
• Size: 10.1 MB
• Stars: 45
• Watchers: 4
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

          
# QuantitaveFinanceExamplesPy

Financial analysis, algorithmic trading, portfolio optimization examples with Python

DISCLAIMER - No Investment Advice Provided

YASAL UYARI - Burada yer alan yatırım bilgi, yorum ve tavsiyeleri yatırım danışmanlığı kapsamında değildir.

## Requirements

Please install requirements from `requirements.txt`.

## References (for both methods and some code fragments)

* Hilpisch, Y. J. (2021). Python for algorithmic trading: From idea to cloud deployment. O'Reilly.

* Jansen, S. (2020). Machine learning for algorithmic trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with python. Packt Publishing.

* Pik, J., & Ghosh, S. (2021). Hands-on financial trading with python. Packt Publishing.

* Velu, R. P., Hardy, M., & Nehren, D. (2020). Algorithmic trading and quantitative strategies. CRC Press, Taylor & Francis Group.

* Brugiere, P. (2021). Quantitative portfolio management: With applications in python. Springer Nature. 

* Dowd, K. (2007). Measuring market risk. John Wiley & Sons.

* Hilpisch, Y. J. (2020). Artificial Intelligence in Finance. O'Reilly.

## Contact

Murat Koptur, [LinkedIn](https://www.linkedin.com/in/muratkoptur/)

Email: [muratkoptur@yandex.com](mailto:muratkoptur@yandex.com?subject=QuantitativeFinanceGithub)

## Examples

**Note**: In all examples, assumed the risk-free rate is zero.

### Calculation Alpha and Beta factors

![01_01](imgs/01_01.png)

### Cointegration

```text

ARCLK.IS and TOASO.IS has cointegration, p-value: 0.04903369798110527

AYGAZ.IS and KCHOL.IS has cointegration, p-value: 0.007029900251131765

FROTO.IS and MAALT.IS has cointegration, p-value: 0.015757028038897322

FROTO.IS and OTKAR.IS has cointegration, p-value: 0.004399007493986555

KCHOL.IS and AYGAZ.IS has cointegration, p-value: 0.007101145930953294

MAALT.IS and FROTO.IS has cointegration, p-value: 0.00783799297255268

OTKAR.IS and FROTO.IS has cointegration, p-value: 0.003094678911810982

OTKAR.IS and TTRAK.IS has cointegration, p-value: 0.04185601871282213

OTKAR.IS and YKGYO.IS has cointegration, p-value: 0.00282083357242191

TTRAK.IS and OTKAR.IS has cointegration, p-value: 0.03639137062922606

TTRAK.IS and YKGYO.IS has cointegration, p-value: 0.03834839887528665

YKGYO.IS and OTKAR.IS has cointegration, p-value: 0.0017665073676291331

YKGYO.IS and TOASO.IS has cointegration, p-value: 0.046004150077470406

YKGYO.IS and TTRAK.IS has cointegration, p-value: 0.027200620035757236

```

### PCA on Returns

![03_01](imgs/03_01.png)

### Volatility calculations

```text

Std.Dev. Estimator:          0.16988244687319595

Classical Estimator:         0.0013349197336295028

Rogers - Satchell Estimator: 0.0009643228704150725

Yang - Zang estimator:       0.0016329397449278639

```

### Volatility-Volume Relationship

![05_01](imgs/05_01.png)

### AR-ARCH models for volatility

![06_01](imgs/06_01.png)

### VWAP

![07_01](imgs/07_01.svg)

### Technical Indicators

![08_01](imgs/08_01.png)

### Denoising Data

![09_01](imgs/09_01.png)

### Trading Signals

![10_01](imgs/10_01.png)

![10_02](imgs/10_02.png)

### Backtesting

![11_01](imgs/11_01.png)

### Pairs Trading

![12_02](imgs/12_02.png)

### Modern Portfolio Theory - Efficient Frontier

![13_01](imgs/13_01.png)

### Value-At-Risk - Expected Shortfall

![14_01](imgs/14_01.png)