Quant Journey & Career
To Do & To Learn List
https://www.youtube.com/watch?v=qvFYzJ8-zbQ
Computer Science
• Language:
Python, SQL, HTML, CSS, Javascript, Angular
• Machine Learning:
Random Forest, Neural Networks, Decision Tree, Clustering, Dimensionality Reduction, Ensemble
• Data Manipulation:
Numpy, SciPy, Pandas, Statsmodel
• Data Visualisation:
Matplotlib, Plotly and Cufflinks, Seaborn, PyPlot, Bokeh
Math
• Calculus and Linear Algebra
• Optimization (Taylor Series, Markov Processes)
• ODE and PDE
• Stochastic Calculus (Martingales, Brownian Motion, Stochastic Integrals, Stochastic Differential Equations, Ito’s Lemma, Feynman-Kac)
• Binomial Asset Pricing
Statistics
• Regression (OLS, GLM, Logistic, and etc.)
• Time-series (ARIMA, GARCH, ECM)
• Nonparametric Regression (Splines, Kernel, Locally Weighted Regression)
• Data Exploration (Density Estimation, Normality Tests, Monte Carlo, Copulas
- Data Cleaning and Reduction (Cluster Analysis and Stats Theory)
Finance
• Equity (Stock Analysis, Diversification, Technical Analysis, Finance Theory)
• Fixed Income (Rate Curves, Pricing, Duration, TVM)
• Derivatives (Black Scholes, BDT, Stochastic Volatility Model, Volatility Smiles and Theory)
• Portfolio Optimization (CVaR, Efficient Frontier)
• Arbitrage Theory and Statistical Arbitrage
• Risk Management (VaR, Statistics, Credit Risk, Market Risk, Liquidity)
Inital List
1.0) Tech Skills
-Python, C++, C#, R, Matlab -> Programming
-Numpy, SciPy, Pandas, quantdsl, statistics -> Data Manipulation
-Statsmodel -> Explore data, estimate statistical models, and perform statistical tests.
-pyfin, vollib, QuantPy, ffn, pynance, tia -> Financial Instruments
-Tensorflow & ML, Keras, Scikit-Learn, Pytorch -> Machine Learning
-ZipLine, QTPyLib, PyAlgoTrade, Pybacktest, bt, backtrader, finmarketpy -> Backtesting
-Ultrafinance, TWP -> Data Collection
-IBridgePy, IbPy -> Interactive Broker Trading
-Blueshift, Quantiacs, Quantopian -> Open Source Python Trading Platforms
-Linux environment & shell scripting
-Pandas-datareader -> FInancial data from Google, World Bank
-TA-Lib -> Technical analysis of financial market data.
-PyMC3 ->Write down models using an intuitive syntax to describe a data generating process.
-MlFinLab -> Turning academic research into practical, easy-to-use libraries
-NLP -> NLTK. TextBlob, spaCy
2.0) Financial Analysis
2.1) Quantitative portfolio management techniques:
2.2) Mathematical models and methods
2.3) Statistical
2.4) Econometric models
2.5) Quantitative-modeling
2.6) Risk-modelling (Model development/validation)
3.0) Trading Strategy
Momentum strategy (Divergence or trend trading)
Reversion strategy.
Forecasting strategy
High-Frequency Trading (HFT) strategy
Stock Market Strategies — Seasonal Anomalies
3.1) Result Analysis
-Calmar Ratio
-Sharpe Ratio
-Drawdown
-Compound Annual Growth Rate (CAGR)
-Distribution of returns,
-Trade-level metrics
3.2) Backtest
Tools: Pandas, zipline and Quantopian.
Pitfalls:
-External events, such as market regime shifts, which are regulatory changes or macroeconomic events
-Liquidity constraints, such as the ban of short sales
-Yourself:
-Overfit a model (optimization bias)
-Ignore strategy rules because you think it’s better like that (interference)
-Introduce information into past data (lookahead bias).
Backtesting Components (Four essential components)
-A data handler, which is an interface to a set of data
-A strategy, which generates a signal to go long or go short based on the data,
-A portfolio, which generates orders and manages Profit & Loss (also known as “PnL”),
-An execution handler, which sends the order to the broker and receives the “fills” or signals that the stock has been bought or sold.
3.3) Optimisation
Improve the model on a continuous basis
-KMeans
-k-Nearest Neighbors (KNN)
-Classification or Regression Trees
-Genetic Algorithm.
-Reinforcement learning, stochastic optimization, Bayesian frameworks, deep learning & machine learning
Working with multi-symbol portfolios
-Just incorporating one company or symbol into your strategy often doesn’t really say much.
-You’ll also see this coming back in the evaluation of your moving average crossover strategy.
Risk management framework
Event-driven backtesting to help mitigate the lookahead bias
4.0) Portfolio Maker
-Deploy Jupyter online
-Automated Trading System -> FXCM, Oanda, Quantopian, Quantconnect
-Running Your Algorithms in Cloud -> AWS EC2 Instance
-Forex Algorithmic Trading -> C++ , MQL4
-Trading Type: Forex, Cyrto, Equity
-Test: MQL4, Quotopian, Quantconnect, Freqtrade
-Algo Platform List
Quantopian (Python)
Backtrader (Python)
Quantconnect (C#, F#)
Quantrocket (Python), $$
-Data provider
Intrinio
Quandl
Norgate Data
-Execution Broker-Dealers
Interactive broker
Alpaca
5.0) Programming competitions
- Kaggle, CrowdAnalytix, CrowdAI, DrivenData
Quantiacs
https://www.quantiacs.com/For-Quants/Quant-Tutorials/Videos.aspx
Quantconnect
Career Path
https://www.youtube.com/watch?v=ptd4XicBUnY
1) Front office/desk quant
2) Model validating quant
3) Research quant
4) Quant developer
5) Statistical arbitrage quant
6) Capital quant
https://www.youtube.com/watch?v=Ubg0fX2mLUQ
https://drive.google.com/file/d/1ENm1RH9rwrnCuSq8cc9awuwekaETEDIA/view
