fix tutorial

docs/source/tutorial/tutorial1.md

@@ -1,20 +1,18 @@
 # Tutorial 1: Intraday Crypto Trading with DeepScalper
+![DeepScalper.png](DeepScalper.png)
 
-## Task
 Intraday trading is a fundamental quantitative trading task, where traders actively long/short one pre-selected financial asset within the same trading day to maximize future profit.
 
-## Algorithm
-DeepScalper contains 4 technical contributions which all together make it better than direct use of RL algorithms.
-- RL optimization with action branching
-- reward function with hindsight bonus
-- intraday market embedding
-- risk-aware auxiliary task
+DeepScalper use deep q network to optimize the reward sum got from reinforcement learning where a hindsight reward is used to capture the long-term porfit trends and embedding from both micro-level and macro-level market information.
 
-Here is the construction of the DeepScalper:
-<div align="center">
-  <img src="https://github.com/TradeMaster-NTU/TradeMaster/blob/main/docs/source/tutorial/DeepScalper.jpg" width = 900 height = 300 />
-</div>
 
 
 
-Here is a [tutorial](https://github.com/DVampire/TradeMasterReBuild/tree/main/tutorial/DeepScalper.ipynb) about how you can build DeepScalper in a few lines of codes using TradeMaster.
+## Notebook and Script
+In this notebook, we implement the training and testing process of DeepScalper based on the TradeMaster framework.
+
+[Tutorial1_DeepScalper](https://github.com/TradeMaster-NTU/TradeMaster/blob/main/tutorial/Tutorial1_DeepScalper.ipynb)
+
+And this is the script for training and testing.
+
+[train.py](https://github.com/TradeMaster-NTU/TradeMaster/blob/1.0.0/tools/algorithmic_trading/train.py)

docs/source/tutorial/tutorial2.md

@@ -1,19 +1,20 @@
 # Tutorial 2: Portfolio Management with EIIE on US stocks
-
-## Task
+![EIIE.png](EIIE.png)
 Portfolio management is the action of continuous reallocation of a capital into a number of financial assets periodically.
 
-## Algorithm
-EIIE contains 2 technical contributions which all together make it better than direct use of RL algorithms.
-- Deterministic Policy Gradient
-- Portfolio-Vector Memor
+The framework consists of the Ensemble of Identical Independent Evaluators
+(EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning
+(OSBL) scheme, and a fully exploiting and explicit reward function.
+
+
+
 
 
-Here is the construction of the EIIE:
-<div align="center">
-  <img src="https://github.com/TradeMaster-NTU/TradeMaster/blob/main/docs/source/tutorial/EIIE.jpg" width = 900 height = 300 />
-</div>
+## Notebook and Script
+In this notebook, we implement the training and testing process of EIIE based on the TradeMaster framework.
 
+[Tutorial2_EIIE](https://github.com/TradeMaster-NTU/TradeMaster/blob/main/tutorial/Tutorial2_EIIE.ipynb)
 
+And this is the script for training and testing.
 
-Here is a [tutorial](https://github.com/DVampire/TradeMasterReBuild/tree/main/tutorial/EIIE.ipynb) about how you can build EIIE in a few lines of codes using TradeMaster.
+[train_eiie.py](https://github.com/TradeMaster-NTU/TradeMaster/blob/1.0.0/tools/portfolio_management/train_eiie.py)

docs/source/tutorial/tutorial5.md

@@ -1,11 +1,14 @@
 # Tutorial 5: High Frequency Trading with Double DQN
 
-## Task
 High Frequency Trading is a fundamental quantitative trading task, where traders actively buy/sell one pre-selected financial periodically in seconds with the consideration of order execution.
 
-## Algorithm
-HFT_DDQN contains 2 technical contributions which all together make it better than direct use of RL algorithms.
-- Double deep q network
-- Regulator from the true q table
+HFT_DDQN use a decayed supervised regulator genereated from the real q table based on the future price information and a double q network to optimizer the portfit margine.
 
-Here is a [tutorial](https://github.com/DVampire/TradeMasterReBuild/tree/main/tutorial/HFT.ipynb) about how you can build DDQN for HFT in a few lines of codes using TradeMaster.
+## Notebook and Script
+In this notebook, we implement the training and testing process of HFTDDQN based on the TradeMaster framework.
+
+[Tutorial5_HFT](https://github.com/TradeMaster-NTU/TradeMaster/blob/main/tutorial/Tutorial5_HFT.ipynb)
+
+And this is the script for training and testing.
+
+[train.py](https://github.com/TradeMaster-NTU/TradeMaster/blob/1.0.0/tools/high_frequency_trading/train.py)