CHANGELOG.md

[0.5.0] - 2024-01-30

Added:

• Added MACD indicator to indicators file.
• Added reward.AccountValueChangeReward object to calculate reward based on the change in the account value.
• Added scalers.ZScoreScaler that doesn't require min and max to transform data, but uses mean and std instead.
• Added ActionSpace object to handle the action space of the agent.
• Added support for continuous actions. (float values between 0 and 1)

Changed:

• Updated all indicators to have config parameter, that we can use so we can serialize the indicators. (save/load configurations to/from file)
• Changed reward.simpleReward to reward.SimpleReward Object.
• Updated state.State to have open, high, low, close and volume attributes.
• Updated data_feeder.PdDataFeeder to be serializable by including save_config and load_config methods.
• Included trading fees into trading_env.TradingEnv object.
• Updated trading_env.TradingEnv to have reset method, which resets the environment to the initial state.
• Included save_config and load_config methods into trading_env.TradingEnv object, so we can save/load the environment configuration.

[0.4.0] - 2024-01-02

Added:

• Created indicators file, where I added BolingerBands, RSI, PSAR, SMA indicators
• Added SharpeRatio and MaxDrawdown metrics to metrics
• Included indicators handling into data_feeder.PdDataFeeder object
• Included indicators handling into state.State object

Changed:

• Changed finrock package dependency from 0.0.4 to 0.4.1
• Refactored render.PygameRender object to handle indicators rendering (getting very messy)
• Updated scalers.MinMaxScaler to handle indicators scaling
• Updated trading_env.TradingEnv to raise an error with np.nan data and skip None states

[0.3.0] - 2023-12-05

Added:

• Added DifferentActions and AccountValue as metrics. Metrics are the main way to evaluate the performance of the agent.
• Now metrics.Metrics object can be used to calculate the metrics within trading environment.
• Included rockrl==0.0.4 as a dependency, which is a reinforcement learning package that I created.
• Added experiments/training_ppo_sinusoid.py to train a simple Dense agent using PPO algorithm on the sinusoid data with discrete actions.
• Added experiments/testing_ppo_sinusoid.py to test the trained agent on the sinusoid data with discrete actions.

Changed:

• Renamed and moved playing.py to experiments/playing_random_sinusoid.py
• Upgraded finrock.render.PygameRender, now we can stop/resume rendering with spacebar and render account value along with the actions

[0.2.0] - 2023-11-29

Added:

• Created reward.simpleReward function to calculate reward based on the action and the difference between the current price and the previous price
• Created scalers.MinMaxScaler object to transform the price data to a range between 0 and 1 and prepare it for the neural networks input
• Created state.Observations object to hold the observations of the agent with set window size
• Updated render.PygameRender object to render the agent's actions
• Updated state.State to hold current state assets, balance and allocation_percentage on specific State

[0.1.0] - 2023-10-17

Initial Release:

• Created the project
• Created code to create random sinusoidal price data
• Created state.State object, which holds the state of the market
• Created render.PygameRender object, which renders the state of the market using pygame library
• Created trading_env.TradingEnv object, which is the environment for the agent to interact with
• Created data_feeder.PdDataFeeder object, which feeds the environment with data from a pandas dataframe