add homegrown example Stock NeurIPS2018

finrl/applications/Stock_NeurIPS2018/README.md

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+We show a workflow of applying RL in algorithmic trading, which is a reproduction and improvement of the process in the [NeurIPS 2018 paper](https://arxiv.org/abs/1811.07522).
+
+# Usage
+
+## Step I. Data
+
+First, run the notebook: *Stock_NeurIPS2018_1_Data.ipynb*. 
+
+It downloads and preprocesses stocks' OHLCV data.
+
+It generates two csv files: *train.csv*, *trade.csv*. You can check the provided two sample files.
+
+## Step II. Train a Trading Agent
+
+Second, run the notebook: *Stock_NeurIPS2018_2_Train.ipynb*. 
+
+It shows how to process the data into an OpenAI gym-style envrionment, and then train a DRL agent.
+
+It will generate a trained RL model .zip file. Here, we also provided a training A2C model in .zip file.
+
+## Step III. Backtest
+
+Finally, run the notebook: *Stock_NeurIPS2018_3_Backtest.ipynb*.
+
+It backtests the trained agent and compares with two baselines: Mean-Variance Optimization and the market DJIA index, respectively.
+
+At the end, it will plot a figure of the portfolio value during the backtest process.