You can be the most sophisticated trader, but without the right mindset your trading results will be inconsistent. To explain this, let us take the example of the field of sport. Performance psychologists help athletes perform at peak levels, while focusing on all aspects of their lives, including personal challenges, eating habits, and post-injury rehabilitation. They closely assess the impact of psychological conditions on athletic performance and the impact of sport on the mind of an athlete.

Trading in the stock market is similar to being a professional athlete. Both are judged on their daily performance. You need to cultivate habits to develop your emotional resilience.

This means that wild fluctuations in the stock market shouldn’t make you jittery or nervous. The ups and downs of the stock market are an integral part of the investment journey. You need courage and agility to stay prepared for any directional movement.

It’s easier said than done. Our brains are hardwired to let emotions take control. When we see red piling up in our positions, the “fight or flight” instinct kicks in. This causes gut reactions, which can be costly and prevent us from achieving our goals. So how do you avoid emotions while trading? Here is a preview.

Treat it like a business

I always stress that investors should see themselves as independent entrepreneurs. It requires a radical change of perspective. If you think of investing as a hobby, you won’t be able to quantify your goals. This could make it difficult to achieve lasting progress.

Write a business plan, list your goals, determine your risk profile and the amount you plan to invest. What will be the source of this capital? How much can you afford to lose? Describing these things on a daily basis will keep fear and greed in check. When you treat trading like a business, you are also less likely to make decisions out of boredom.

Recognize emotions
You should consider putting some time between the momentum to act and your investment decision. This is true whether you want to buy the downside or sell during a rise. Various studies have indicated that breathing exercises can help you stay calm, productive, and rational while dealing with stressful situations. Research further proves that different forms of breathing are associated with different emotions, so the way you breathe can impact how you feel. Changing the rhythm of your breathing can signal relaxation to your brain. This will give you time to reassess your approach to investing.

Market research is crucial
Emotions sometimes creep in when you don’t know what to do in a particular market situation. This is why you need to stay up to date on the fundamentals of the market. Focusing on companies with great quarterly results may not give you the big picture. Understand the underlying macroeconomic factors, both domestic and global. Be aware of all geopolitical developments that can trigger market volatility.

Look for new educational resources, such as analysts’ opinions on undervalued stocks, price predictions, and economic analysis. Improve your research with a new newsletter every now and then. You may find something that fundamentally changes the way you invest, or something that you don’t agree with.

Either way, when you make a concentrated effort to always learn something new about investing or the markets, you will have a new perspective and a new confidence. It makes you a more educated trader and breaks the cycle of emotional trading.

Always remember the past
When the market plunges the next time around, remember it’s not the first and it won’t be the last. Globally, the stock markets have overcome so many obstacles over the decades. For example, Indian stock markets experienced the largest one-day decline in absolute terms on March 12, 2020, as the WHO declared Covid-19 a global pandemic. The benchmark Sensex was down 8.2% to trade at 32,778 points, and Nifty 50 was down 8.3% to trade at 9,590 points. But as of March 15, 2021, Nifty 50 was trading at 14,772.62 points, more than pre-pandemic levels.

The stock market has recovered and reached new highs after each global crisis. Also, keep in mind that even if the stock market goes down, your overall portfolio might not go down. If you have a well-diversified investment portfolio, comprising stocks, bonds, and other assets, your panic can subside considerably.

Ignore background noise

You may have read various buzzwords and phrases in articles or books before. Stick to your trading plan. Have patience and discipline. Be in the area.

They are all logical, perhaps even encouraging. However, unless your mind is at home at home and you are in good mental and physical health, you cannot follow these steps. So, consider a few non-market tips to get your head in, like getting regular exercise, doing something creative in your spare time, reading a book, and more. These things can keep your mind from focusing on the markets.

Finally, accept that mistakes are made by even the best business minds. How to react to these mistakes differentiates a good trader from an excellent one. Learn from them and keep looking ahead.

(The author is Managing Director and CEO, HDFC Securities)

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The main focus of AI is the development of computational functions associated with human intelligence, such as reasoning, learning and problem solving, which can be particularly useful for markets. Trading and investing in the market only requires a series of reasoning and calculations, based on data and solving the problem of predicting the future direction of current stock prices. Fundamental and manual technical analysis is going out of fashion these days. The application of machine learning technology in trading or stock market is used so that the system automatically learns the complexity of the trade and improves its algorithms to assist with the best trading gift. In the last decade, there seemed to be a use of the wallet of traders, so that everyone could earn their profits. But, with the help of AI, one can perfectly analyze the underlying data points presented very quickly and accurately.

Using such data points, we can analyze current market trends and train high speed patterns, which are the two necessary elements generally used for smart trading. Using headlines from news channels and news sources, reviews from social media, and comments on other platforms, AI can analyze the action by performing sentiment analysis on that data. Machine learning usually stores the results and metrics that gave those results and can better analyze the stock market.

Data often helps to find a better solution, especially in probability-based and sentiment-based activities, such as stock trading. But to this day, financial engineers also believe that it is impossible for a machine, left to itself, to beat the stock market. With the rise of technology, incredibly powerful computers can process almost countless data points in a matter of minutes. This means that they are also very capable of detecting historical and replicating patterns for intelligent trading in the market which are often hidden from ordinary human investors. We humans are simply not able to process such data or see these patterns at the same rate as a technologically capable machine. AI can evaluate and analyze thousands of stocks in a matter of moments, and so this technology adds even more speed to trading. Today, every millisecond counts, and with AI as a means of automated trading, it’s a wonder. AI is already learning to continually improve on its own mistakes. It deploys automated trading assistant robots and is constantly working to improve its performance by refining programming and entering huge masses of new data.

What is Open AI Gym Anytrading?

AnyTrading is an Open Source collection of OpenAI Gym environments for reinforcement learning based trading algorithms. The trading algorithms are mainly implemented on the basis of two of the biggest markets present: FOREX and Stock. AnyTrading aims to provide Gym environments to improve and facilitate the process of developing and testing reinforcement learning based algorithms in the field of market trading. This is achieved by implementing it on three Gym environments: TradingEnv, ForexEnv and StocksEnv. AnyTrading can help you learn about stock market trends and perform powerful analysis, providing in-depth insights for data-driven decisions.

Getting started with the code

In this article, we will implement a reinforcement learning based market trading model, in which we will create a trading environment using OpenAI Gym AnyTrading. We will use historical GME price data, then train and evaluate our model using reinforcement learning agents and the gymnastics environment. The following code is partly inspired by a video tutorial on Gym Anytrading, the link of which can be found here.

Library installation

The first essential step would be to install the necessary library. To do this, you can run the following lines of code,

! pip install tensorflow-gpu == 1.15.0 tensorflow == 1.15.0 stable-baselines gym-anytrading gym

Stable-Baselines will provide us with the reinforcement learning algorithm and Gym Anytrading will provide us with our trading environment

Import dependencies

Now let’s install the required dependencies to create a basic framework for our model, and we’ll use the A2C reinforcement learning algorithm to build our market trading model.

# Importing Dependencies
import gym
import gym_anytrading
# Stable baselines - rl stuff
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C
# Processing libraries
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

Processing of our dataset

Now, with our pipeline setup, let’s load our GME Market data. You can download the dataset using the link here. You can also use other relevant data sets such as Bitcoin data to run this model.

#loading our dataset
df = pd.read_csv('/content/gmedata.csv')
#viewing first 5 columns
df.head()

#converting Date Column to DateTime Type
df['Date'] = pd.to_datetime(df['Date'])
df.dtypes

Go out :

Date      datetime64[ns]
Open             float64
High             float64
Low              float64
Close            float64
Volume            object
dtype: object

#setting the column as index
df.set_index('Date', inplace=True)
df.head()

We will now transmit the data and create our gym environment for our agent to train later.

#passing the data and creating our environment
env = gym.make('stocks-v0', df=df, frame_bound=(5,100), window_size=5)

Setting the window size parameter will specify how many previous price references our trading bot will have so that it can decide to place a trade.

Test our environment

Now with our model setup, let’s test our basic environment and deploy our reinforcement learning agent.

#running the test environment
state = env.reset()
while True: 
    action = env.action_space.sample()
    n_state, reward, done, info = env.step(action)
    if done: 
        print("info", info)
        break
        
plt.figure(figsize=(15,6))
plt.cla()
env.render_all()
plt.show()

As we can see, our agent RL bought and sold stocks at random. Our profit margin appears to be greater than 1, so we can determine that our bot has made us profit from the trades it has made. But these were random steps, now let’s properly train our model to get better trades.

Shaping our environment

Configure our environment to train our reinforcement learning agent,

#setting up our environment for training 
env_maker = lambda: gym.make('stocks-v0', df=df, frame_bound=(5,100), window_size=5)
env = DummyVecEnv([env_maker])

#Applying the Trading RL Algorithm
model = A2C('MlpLstmPolicy', env, verbose=1) 
 
#setting the learning timesteps
model.learn(total_timesteps=1000)

---------------------------------
| explained_variance | 0.0016   |
| fps                | 3        |
| nupdates           | 1        |
| policy_entropy     | 0.693    |
| total_timesteps    | 5        |
| value_loss         | 111      |
---------------------------------
---------------------------------
| explained_variance | -2.6e-05 |
| fps                | 182      |
| nupdates           | 100      |
| policy_entropy     | 0.693    |
| total_timesteps    | 500      |
| value_loss         | 2.2e+04  |
---------------------------------
---------------------------------
| explained_variance | 0.0274   |
| fps                | 244      |
| nupdates           | 200      |
| policy_entropy     | 0.693    |
| total_timesteps    | 1000     |
| value_loss         | 0.0663   |

#Setting up the Agent Environment
env = gym.make('stocks-v0', df=df, frame_bound=(90,110), window_size=5)
obs = env.reset()
while True: 
    obs = obs[np.newaxis, ...]
    action, _states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    if done:
        print("info", info)
        break


#Plotting our Model for Trained Trades
plt.figure(figsize=(15,6))
plt.cla()
env.render_all()
plt.show()

As we can see here, our skilled agent is now doing much better trades and a lot less random trades, giving us profit at the same time with a lot more awareness of when to buy and when to sell the stock.

End Notes

In this article, we have tried to understand how artificial intelligence can be applied to market trading to help leverage the art of buying and selling. We have also created a reinforcement learning model whereby our skilled agent can buy and sell stocks, reserving us profits simultaneously. The following implementation above can be found as a Colab notebook, accessible using the link here.

Good learning!

The references

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