Deep Learning for Stock Prediction/ Quantitative Trading Strategies

Part 1: Data Collection

• Technical historical (price, volume, OHLC changed percentage)

• Technical Indicators (MA, MACD, Momentum, CCI, RSI, …)

• Fundamental data (Key ratios, finance statement)

• Macroeconomics ratios (GDP, CPI, Job, Interest rate, …)

• Sentiment data (Google news, Social networks)

Part 2: Data Preprocessing

• Dummy variables

• Concatenate data frames

• Missing data

• Scaling data

• Feature processing using ensemble feature selection

o Increase the dimension

o Reduce the dimension

o Avoid overfitting

o Select the optimal feature selection. Since then we also find out some strategies (Decision tree/Association Rules, etc.

Part 3: Machine Leaning and DL Models

1. ML model

• Regression


• Random Forest



• Deep Learning (LTSM,HTM, Tensorfow, etc)

2. Evaluation

Part 4: Portfolio Construction/Optimization

• Strategies

• Portfolio Combination and permutation

• Portfolio Allocation

• Portfolio Optimization

Part 5: Backtest

Quantopian Zipline

Require freelancer have experienced in Quantitative Trading.
Prefer Academic professional in Quants/Machine Learning in Finance

If you are confident please inbox direct to me.

Evner: Datavidenskab, Finansanalytiker , Maskinoplæring, Python, Statistisk analyse

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Om arbejdsgiveren:
( 1 bedømmelse ) TP, Taiwan

Projekt ID: #17328560

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