Deep q learning on determining buy/sell signal and placing orders
This project is inspired by the paper: A Multi-agent Q-learning Framework for Optimizing Stock Trading Systems
The trading system takes 4 agents: buy signal agent, buy order agent, sell order agent, sell signal agent.
- Buy signal agent makes the long decision by considering the state of stocks in current day
- Buy order agent determines the buy price after buy signal agent gave the buy signal
- Sell signal agent makes the short decision ( after holding the stocks, no short sell )
- Sell order agent determines the sell price after sell signal agent gave the sell signal
This strategy makes use of the price difference between the current price to the previous high/low turning points to determine the buy/ sell signal
Different technical indicators, e.g. sma, high/low difference...
State of stock feeds into BSA, an action is generated as below
| Agent Output | Action |
|---|---|
| BUY | Trigger Buy Order Agent 2) |
| NOT-BUY | Go to 1) with next date of state |
BOA takes the same state from BSA and determine the buy price of stocks according to the below formula
buy price = ma5 + action / 100 * ma5
where action is the output of the agent
The order is placed the next day after buy signal is generated in BSA.
| Buy Price | Action |
|---|---|
| > next day's low price | Order is executed, trigger Sell Signal Agent |
| < next day's low price | Order is not executed, go back to 1) |
SSA takes state of stock starting with the day after the buy order is executed, action is generated as below
| Agent Output | Action |
|---|---|
| HOLD | Go to 3) |
| SELL | Trigger Sell Order Agent 2) |
SOA takes the same state from SSA and determine the sell price of stocks according to the below formula
sell price = ma5 + action / 100 * ma5
The order is placed the next day after sell signal is generated in SSA.
| Sell Price | Action |
|---|---|
| < next day's high price | Order is executed, position is closed, go to 1) |
| > next day's high price | Order is executed at that day's close instead, position is closed, go to 1) |
rewards = 0 if order is not successfully placed.
rewards = ((1-transactional cost) * sell price - buy price) / buy price if position is closed by SOA
price diff = buy price - next day's low price
rewards = exp(-100 * price diff / low price) if price diff >= 0
rewards = 0 if price diff < 0
rewards = rate of change of close price
price diff = sell price - next day's high price
rewards = exp(100 * price diff / high price) if price diff <= 0
rewards = 0 if price diff > 0
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent