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Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Applications
Subcategory
Specialists
Keywords
Finance, RL
Stars
921
Forks
341
Contributors
1
Created date
2017-09-01
Updated date
2026-10-08
Stars in 1 day
0
Stars in 7 days
0
Rank
12,622
Previous rank
12,537
Rank change
-85
7-day stars rank
13,516
Previous 7-day stars rank
13,524
7-day stars rank change
8
Total stars rank
11,849
Previous total stars rank
11,829
Total stars rank change
-20
README summary
Q-Trader An implementation of Q-learning applied to (short-term) stock trading. The model uses n-day windows of closing prices to determine if the best action to take at a given time is to buy, sell or sit. As a result of the short-term state representation, the model is not very good at making decisions over long-term trends, but is quite good at predicting peaks and troughs. Results Some examples of results on test sets: S
&P 500, 2015. Running the Code To train the model, download a training and test csv files from Yahoo! Finance into Then when training finishes (minimum 200 episodes for results): References Deep Q-Learning with Keras and Gym - Q-learning overview and Agent skeleton code
Frequently asked questions
How many stars and forks are recorded?
edwardhdlu/q-trader has 921 stars and 341 forks.
What is the repository rank?
edwardhdlu/q-trader is ranked 12,622.
What language and category are recorded?
edwardhdlu/q-trader has 921 stars, uses Python and belongs to Applications.
Which repository is closest in rank?
edwardhdlu/q-trader is compared with woheller69/whoBIRD, ranked 12,618 with 922 stars.
README
Q-Trader
An implementation of Q-learning applied to (short-term) stock trading. The model uses n-day windows of closing prices to determine if the best action to take at a given time is to buy, sell or sit.
As a result of the short-term state representation, the model is not very good at making decisions over long-term trends, but is quite good at predicting peaks and troughs.
Results
Some examples of results on test sets:
S&P 500, 2015. Profit of $431.04.
Alibaba Group Holding Ltd, 2015. Loss of $351.59.
Apple, Inc, 2016. Profit of $162.73.
Google, Inc, August 2017. Profit of $19.37.
Running the Code
To train the model, download a training and test csv files from Yahoo! Finance into data/
mkdir model
python train ^GSPC 10 1000
Then when training finishes (minimum 200 episodes for results):
S&P 500, 2015. Profit of $431.04.
Alibaba Group Holding Ltd, 2015. Loss of $351.59.
Apple, Inc, 2016. Profit of $162.73.
Google, Inc, August 2017. Profit of $19.37.