Deep Reinforcement Learning In Trading – Quantra

Deep Reinforcement Learning In Trading – Quantra

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Description:

Through This Course, You Will Learn To Master:

Finance and Math skills: Stochastic gradient descent, the Mean squared error, and so on.
The application of Python: pandas, numpy, matplotlib, datetime, TA-lib, Tensorflow, Keras, SGD, and so on.
Reinforcement Learning model: Double Q–Learning, Artificial Neural Networks, State, Rewards, Actions, Experience Replay, Exploration vs. Exploitation

The Content Of The Deep Reinforcement Learning In Trading By Quantran Covers:

How the methods of deep reinforcement learning generate the advantages to trade more profitably.
Instructions on input features for the buildup of a state, including candlesticks, technical indicators and time signatures.
The whole process of creating and assembling state includes the development of input features in Python, how to increase the accuracy of the market move projection by the neutral network.
Insights into the concepts of artificial neural networks for the variations of inputs.
Guidelines on backtesting logic shed light on how to combine elements of the reinforcement learning model.
The generation of RL model patterns and how to leverage the random data generator, etc. are openly shared for performance analysis and synthetic data.
How to trade with the automated strategy to cut down on time and effort for the acceleration of profit mom

Forex Trading – Foreign Exchange Course

Want to learn about Forex?

Foreign exchange, or forex, is the conversion of one country’s currency into another.
In a free economy, a country’s currency is valued according to the laws of supply and demand.
In other words, a currency’s value can be pegged to another country’s currency, such as the U.S. dollar, or even to a basket of currencies.
A country’s currency value may also be set by the country’s government.
However, most countries float their currencies freely against those of other countries, which keeps them in constant fluctuation.