Posted December February 25, 2021 by Rokas Balsys

Integrating and Optimizing Technical Indicators to Automated Bitcoin Trading Bot that could beat the Market!

In this tutorial, we will continue developing a Bitcoin trading bot. We’ll integrate more technical indicators into our code, we try a newly proposed normalization technique, and of course, I’ll slightly modify the whole code that it could be much easier to test our trained models.

Welcome back to my 7th tutorial part with the Reinforcement Learning Bitcoin trading bot. To get to the point where we are now, I had to write thousands of code lines and spend thousands of hours while training my models. But if someone would be told me, that to get to the current 7th tutorial it would have cost me so much, I am not sure if I would be starting doing this tutorial series.

On the other hand, I’m proud of myself that I didn’t give up and no matter how hard it was or I didn’t know the solutions from a particular moment, I tried to find the strength to move forward with my projects. This is called self-motivation, and I did this not for myself, but for everyone who will read my tutorial series.

Looking at what we have done so far I can’t believe it by myself. We actually created a Bitcoin trading bot that could beat the market in our simulation! So far we created a Reinforcement Learning cryptocurrency trading environment where we can simulate our trades by using multiprocessing to speed everything up. Also, we found an easy way how we can download Historical marked data, we tested several (CNN, LSTM, Dense) Neural Networks architectures to measure their performance and all this is only a small part of what we have done!

So far, trades in our simulation were taking place on perfect terms, so in this tutorial part, I decided to insert order fees into our model, add more uncorrelated indicators, implement a solution how we could normalize our training/testing data, and implement a solution how we can test our different models without remembering training settings. So this part might be one of the most interesting and exciting!

Normalizing Data

Until this moment, I didn’t use any normalization techniques, I simply divided all values by 40k. But I want to point out, that time series data is not stationary (you can google what it means), this means that it’s hard for a machine learning model to predict a downtrend if it was learned on an uptrend data while training.

We can solve this by using differencing and transformation techniques to convert our data to a more normal distribution form.

This is how our market data looks like if we’ll plot only Close price:

Bitcoin Differenced Close price

Differencing is a process where we subtract the derivative (rate of return) at each time-step from the value at a previous time-step. We do this with one simple line:

df["Close"] = df["Close"] - df["Close"].shift(1)


As a result, this should remove a trend and receive the following results:

Bitcoin Differenced Close price

Results look quite interesting and seem that the visual trend was removed. However, the data still has a clear seasonality. We can try to remove that by taking the logarithm at every time-step before differencing our data. It’s very similar to the above line:

df["Close"] = np.log(df["Close"]) - np.log(df["Close"].shift(1))


Now we receive the following chart:

Bitcoin Logged Differenced Close price

Now it’s obvious that it’s impossible for us humans to tell that in this chart is our Bitcoin historical data. In the last step, we’ll take this data and simply normalize this data by putting it between 0 and 1:

Bitcoin Logged Differenced Normalized Close price

And there are few lines of code I used to receive all 4 above plots:

if __name__ == "__main__":
# testing normalization technieques
df = df.dropna()
df = df.sort_values('Date')

#df["Close"] = df["Close"] - df["Close"].shift(1)
df["Close"] = np.log(df["Close"]) - np.log(df["Close"].shift(1))

Min = df["Close"].min()
Max = df["Close"].max()
df["Close"] = (df["Close"] - Min) / (Max - Min)

fig = plt.figure(figsize=(16,8))
plt.plot(df["Close"],'-')
ax=plt.gca()
ax.grid(True)
fig.tight_layout()
plt.show()


Technical Indicators

In my 5th tutorial of this tutorial series, I showed you, how we can insert indicators into our market data. At that tutorial, I inserted 5 indicators: SMA, Bollinger Bands, Parabolic SAR, MACD, and RSI. Also, at the end of that tutorial, I mentioned that later we’ll try to insert more of them and I think this tutorial is a great time for this task.

Usually, technical indicators are used for some kind of technical analysis, but we’ll call this “Feature engineering” because we’ll try to extract only the least correlated indicators from a batch, then we’ll normalize them with the above-given technique and everything will be fed to our Reinforcement Learning agent.

To choose the technical indicators that we’ll use we are going to compare the correlation of all 42 (at the moment of writing this tutorial) technical indicators in the ta library. The simplest way is to use pandas and seaborn libraries to find the correlation between each indicator of the same type (trend, volatility, volume, momentum, others). Then we’ll select only the least correlated indicators from each type. In my opinion, this way we can get as much benefit out of these technical indicators as possible, without adding too much noise to our state size.

Correlation and why it's important?

One of the fastest ways to enhance a machine learning model is to identify and reduce the dataset features that are highly correlated. These features add noise and inaccuracy to our model, which in turn makes it harder to achieve the desired result.

When two independent features have a strong relationship, they are considered positively or negatively correlated. It’s recommended that highly correlated variables should be avoided when developing models because they can skew the output. If there are two independent variables representing the same event, it can cause “noise” or inaccuracy in the model. The models rely only on external information to generate useful output and having collinear (correlating) variables can increase variation in at least one of the regression outputs. This makes it difficult to understand which variable actually influences the dependent variable, making it difficult to assess the usefulness of the model.

I am not going deep into explaining why where and how, there is plenty of information elsewhere. I’ll keep on practical stuff.

First I’ll shortly explain my function that I use to drop correlated features and plot the visualization:

def DropCorrelatedFeatures(df, threshold, plot):
df_copy = df.copy()

# Remove OHCL columns
df_drop = df_copy.drop(["Date", "Open", "High", "Low", "Close", "Volume"], axis=1)

# Calculate Pierson correlation
df_corr = df_drop.corr()

columns = np.full((df_corr.shape[0],), True, dtype=bool)
for i in range(df_corr.shape[0]):
for j in range(i+1, df_corr.shape[0]):
if df_corr.iloc[i,j] >= threshold or df_corr.iloc[i,j] <= -threshold:
if columns[j]:
columns[j] = False

selected_columns = df_drop.columns[columns]

df_dropped = df_drop[selected_columns]

if plot:
# Plot Heatmap Correlation
fig = plt.figure(figsize=(8,8))
ax = sns.heatmap(df_dropped.corr(), annot=True, square=True)
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
fig.tight_layout()
plt.show()

return df_dropped


As you can see, while reading the above function, first I remove OHCL columns from my panda's data frame, it’s not necessary to calculate the correlation for them. Next, calculating correlation is as simple as writing one line of code: df_corr = df_drop.corr(). That’s it, now we need to drop indicators that are above our threshold. I believe there should be something similar and simple as calculating correlation, but I couldn’t find that, so I simply use a for loop to do that. And lastly, I wrote code lines for beautiful heatmap visualization, actually this was done specifically for this tutorial. I’ll use this function to plot the heatmap of each indicator type.

Trend indicators

The biggest part of whole indicators in ta library is trend indicators, in total there are 14 given indicators. Trend indicators tell us which direction the market is moving in, of course, if there is a trend at all. I will not name each of these indicators, this would be a waste of my and your time. So, below is a function that we will use to get a Heatmap of the trend indicators:

def get_trend_indicators(df, threshold=0.5, plot=False):
df_trend = df.copy()

df_trend["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator()
df_trend["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator()
df_trend["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator()

df_trend = add_trend_ta(df_trend, high="High", low="Low", close="Close")

return DropCorrelatedFeatures(df_trend, threshold, plot)


As you can see, here I added 3 of my own custom indicators sma7, sma25 and sma99, in a similar way, you can add your own indicators. So, probably you may be asking right now, how to use this function, right? Actually, it can’t be simpler, we do it following:

df = pd.read_csv('./BTCUSD_1h.csv')
df = df.sort_values('Date')
get_trend_indicators(df, threshold=0.5, plot=True)


I chose to use a threshold of 0.5 and a plot as True, just for visualization. By running the above code we get the following heatmap as a result:

Before calculating the heatmap there were 14+3 my own indicators, in total this was 17 indicators, after dropping correlated ones, there left only 7 of them. This means that 59% of all trend indicators were correlating.

Volatility indicators

The second batch of indicators is 5 volatility indicators. That's a special form of technical indicators, that measure how far an asset strays from its mean directional value. This might sound complicated but it is quite simple: When an asset has high volatility, it strays far from its average direction. For example, an earthquake has high volatility compared to normal weather conditions. Very similar to trend indicators, I created a function that we will use to get a Heatmap of the volatility indicators:

def get_volatility_indicators(df, threshold=0.5, plot=False):
df_volatility = df.copy()

# ...

df_volatility = add_volatility_ta(df_volatility, high="High", low="Low", close="Close")

return DropCorrelatedFeatures(df_volatility, threshold, plot)


In the same way as before we need to run this above function in the following way:

df = pd.read_csv('./BTCUSD_1h.csv')
df = df.sort_values('Date')
get_volatility_indicators(df, threshold=0.5, plot=True)


Matplotlib should give us the following results:

Before calculating the volatility heatmap there were 5 indicators, as a result, we see the same count of indicators. These are excellent results, which means that all our indicators are calculated by using different techniques that give us such uncorrelated features. This means that none of all volatility indicators were correlating.

Volume indicators

The third batch is well-known volume indicators. The volume shows us the number of shares security has been traded in a given time period. Volume indicators are simple mathematical formulas that are visually represented in most commonly used trading platforms. Same way as before I created a function that we will use to get a Heatmap of the volume indicators:

def get_volume_indicators(df, threshold=0.5, plot=False):
df_volume = df.copy()

# ...

df_volume = add_volume_ta(df_volume, high="High", low="Low", close="Close", volume="Volume")

return DropCorrelatedFeatures(df_volume, threshold, plot)


We run this above function in the following way:

df = pd.read_csv('./BTCUSD_1h.csv')
df = df.sort_values('Date')
get_volume_indicators(df, threshold=0.5, plot=True)


Matplotlib should give us the following results:

Before calculating the heatmap there were 9 indicators, after dropping correlated ones, there left 7 of them. This means that only 22% of all volume indicators were correlating. That’s an impressive result, even now we can see that we could decrease the threshold but the count of indicators will stay the same because they are highly uncorrelated.

Momentum indicators

Momentum indicators show the movement of the price over time and how strong those movements were, are, or will be, regardless of the direction the price is moving. It is said that momentum indicators are also specifically useful because they help traders and analysts recognize points where the market might reverse. We add these indicators with the following function:

def get_momentum_indicators(df, threshold=0.5, plot=False):
df_momentum = df.copy()

# ...

df_momentum = add_momentum_ta(df_momentum, high="High", low="Low", close="Close", volume="Volume")

return DropCorrelatedFeatures(df_momentum, threshold, plot)


We run this above function in the following way:

df = pd.read_csv('./BTCUSD_1h.csv')
df = df.sort_values('Date')
get_momentum_indicators(df, threshold=0.5, plot=True)


Matplotlib should give us the following results:

Before calculating the heatmap there were 11 indicators, after dropping correlated ones, there left only 4 of them. This means that 64% of all momentum indicators were correlating, this means that momentum indicators are the most correlated.

Other indicators

The last batch of indicators is Daily Return (DR), Daily Log Return (DLR), Cumulative Return (CR). I am not sure what they are about, but anyway, I don’t see a reason why we shouldn’t add them to our model with the following function:

def get_others_indicators(df, threshold=0.5, plot=False):
df_others = df.copy()

# ...

return DropCorrelatedFeatures(df_others, threshold, plot)


We run this above function in the following way:

df = pd.read_csv('./BTCUSD_1h.csv')
df = df.sort_values('Date')
get_others_indicators(df, threshold=0.5, plot=True)


Matplotlib should give us the following results:

There is nothing much to say about this, because there were only 3 indicators, that one of them was highly correlated.

In case you want to calculate the correlation between all indicators without separating them into different groups, you may run the following function:

def get_all_indicators(df, threshold=0.5, plot=False):
df_all = df.copy()

# ...

df_all = add_all_ta_features(df_all, open="Open", high="High", low="Low", close="Close", volume="Volume")

return DropCorrelatedFeatures(df_all, threshold, plot)


Everything is quite the same as before.

So, now, when we have all the functions needed to calculate and drop correlation from each indicator group, we need to run the following function:

def indicators_dataframe(df, threshold=0.5, plot=False):
trend       = get_trend_indicators(df, threshold=threshold, plot=plot)
volatility  = get_volatility_indicators(df, threshold=threshold, plot=plot)
volume      = get_volume_indicators(df, threshold=threshold, plot=plot)
momentum    = get_momentum_indicators(df, threshold=threshold, plot=plot)
others      = get_others_indicators(df, threshold=threshold, plot=plot)
#all_ind = get_all_indicators(df, threshold=threshold)

final_df = [df, trend, volatility, volume, momentum, others]
result = pd.concat(final_df, axis=1)

return result


The above function gonna merge all our indicators into one beautiful panda's data frame, which we’ll use further for our RL Bitcoin trading bot model.

Before moving ahead I would like to come back to normalization. As I mentioned at the beginning, I offered another normalization method that we also gonna use to normalize indicators. But here comes another problem, that our indicators might have negative values, and when we try to apply the logarithm to these negative values we get NaN result. So I simply decided not to use logarithm if the resulting values are negative with the following function:

def Normalizing(df_original):
df = df_original.copy()
column_names = df.columns.tolist()
for column in column_names[1:]:
# Logging and Differencing
test = np.log(df[column]) - np.log(df[column].shift(1))
if test[1:].isnull().any():
df[column] = df[column] - df[column].shift(1)
else:
df[column] = np.log(df[column]) - np.log(df[column].shift(1))
# Min Max Scaler implemented
Min = df[column].min()
Max = df[column].max()
df[column] = (df[column] - Min) / (Max - Min)

return df


Training and testing

Because I changed the way how we insert indicators into our dataset and how we normalize our dataset there were a lot of places where I made small changes in code, I decided not to mention these changes in this tutorial, because this tutorial would be only about changes. But I will mention the largest changes.

In all previous tutorial series parts when we were starting to train our model, all parameters were written into parameters.txt file, I decided instead of writing simply into txt, do it in JSON structure:

def start_training_log(self, initial_balance, normalize_value, train_episodes):
# save training parameters to Parameters.json file for future
current_date = datetime.now().strftime('%Y-%m-%d %H:%M')
params = {
"training start": current_date,
"initial balance": initial_balance,
"training episodes": train_episodes,
"lookback window size": self.lookback_window_size,
"depth": self.depth,
"lr": self.lr,
"epochs": self.epochs,
"batch size": self.batch_size,
"normalize value": normalize_value,
"model": self.model,
"comment": self.comment,
"saving time": "",
"Actor name": "",
"Critic name": "",
}
with open(self.log_name+"/Parameters.json", "w") as write_file:
json.dump(params, write_file, indent=4)


While using JSON structure it’s much easier for us to load our training setting when we want to test a particular model that we trained in the past. So, this JSON improvement led me to change test_agent and test_multiprocessing functions, now we’ll need only to call them with fewer parameters.

In my previous tutorials I received a lot of comments, that I am not considering order fees while doing trades, I decided that it’s a good place to finally add this. So I simply made small changes in my step function:

def step(self, action):
...
if action == 0: # Hold
pass

elif action == 1 and self.balance > self.initial_balance*0.05:
# Buy with 100% of current balance
self.crypto_bought = self.balance / current_price
self.crypto_bought *= (1-self.fees) # substract fees
self.balance -= self.crypto_bought * current_price
self.crypto_held += self.crypto_bought
self.trades.append({'Date' : Date, 'High' : High, 'Low' : Low, 'total': self.crypto_bought, 'type': "buy", 'current_price': current_price})
self.episode_orders += 1

elif action == 2 and self.crypto_held*current_price> self.initial_balance*0.05:
# Sell 100% of current crypto held
self.crypto_sold = self.crypto_held
self.crypto_sold *= (1-self.fees) # substract fees
self.balance += self.crypto_sold * current_price
self.crypto_held -= self.crypto_sold
self.trades.append({'Date' : Date, 'High' : High, 'Low' : Low, 'total': self.crypto_sold, 'type': "sell", 'current_price': current_price})
self.episode_orders += 1


You might see that there are two new lines:

• self.crypto_bought *= (1-self.fees) when we do buy action;
• self.crypto_sold *= (1-self.fees) when we do sell action.

In both actions, we subtract fees from our balance, whatever it’s held in crypto or in cash. In most exchanges, this fee is around 0.1%, but if you need you can search for self.fees line in code and change to whatever you want.

Also, you may notice that I removed all the stuff that was related to punish_value, that was used to motivate our bot to do trades instead of holding bitcoin until the end.

Now, because I use a lot of indicators, data preparation is much more difficult, the example you can see below:

if __name__ == "__main__":
df = df.dropna()
df = df.sort_values('Date')

#df = AddIndicators(df) # insert indicators to df
df = indicators_dataframe(df, threshold=0.5, plot=False) # insert indicators to df
depth = len(list(df.columns[1:])) # OHCL + indicators without Date

df_nomalized = Normalizing(df[99:])[1:].dropna() # we cut first 100 bars to have properly calculated indicators
df = df[100:].dropna() # we cut first 100 bars to have properly calculated indicators

lookback_window_size = 100
test_window = 720*3 # 3 months

# split training and testing datasets
train_df = df[:-test_window-lookback_window_size]
test_df = df[-test_window-lookback_window_size:]

# split training and testing normalized datasets
train_df_nomalized = df_nomalized[:-test_window-lookback_window_size]
test_df_nomalized = df_nomalized[-test_window-lookback_window_size:]

# multiprocessing training/testing. Note - run from cmd or terminal
agent = CustomAgent(lookback_window_size=lookback_window_size, lr=0.00001, epochs=5, optimizer=Adam, batch_size=32, model="CNN", depth=depth, comment="Normalized")
train_multiprocessing(CustomEnv, agent, train_df, train_df_nomalized, num_worker = 32, training_batch_size=500, visualize=False, EPISODES=400000)

test_multiprocessing(CustomEnv, CustomAgent, test_df, test_df_nomalized, num_worker = 16, visualize=False, test_episodes=1000, folder="2021_02_11_15_40_Crypto_trader", name="", comment="")


Instead of calling the AddIndicators function, now I run the indicators_dataframe function, which in the current example adds 30 different indicators. I call this indicators count - depth, that we use to construct the right state_size of our model.

You might see that now we have train_df and train_df_normalized data frames. Train_df is without normalization — mostly is used only while rendering beautiful visualization and calculating rewards while our model is training. Train_df_normalized is normalized data, that we use only for training.

To start the training process, we need to create an agent with CustomAgent class with our parameters, and simply feed everything to train_multiprocessing functions.

Actually, I trained an agent with totally the same parameters that I used in my previous tutorial, so we could compare if there is any performance improvement coming from more indicators and a new normalization technique. Here is my Parameters.json file with all our indicators of 2021_02_18_21_48_Crypto_trader model:

{
“training start”: “2021–02–18 21:48”,
“initial balance”: 1000,
“training episodes”: 400000,
“lookback window size”: 100,
“depth”: 30,
“lr”: 1e-05,
“epochs”: 5,
“batch size”: 32,
“normalize value”: 40000,
“model”: “CNN”,
“comment”: “Normalized, no punish value”,
“saving time”: “2021–02–21 05:09”,
}


That’s right, I trained the model for 400k training steps, which took around 55 hours… The best model was named 3906.52_Crypto_trader and testing results for 3 months were: 1162.08$at the end, that’s not that nice as I expected... I made a chart, where I show reward, episode orders, and no profit episodes for each month: As we can see, we made a profit in the first month, but another two months were not that great, even if there was a downtrend we want that our bot could deal with it... Then I decided that I’ll train another model by using AddIndicators instead indicators_dataframe function as in the previous tutorial. The only difference from the previous tutorial will be that now we’ll use a new normalization technique. Here is my Parameters.json file with all our indicators of 2021_02_21_17_54_Crypto_trader model: { “training start”: “2021–02–21 17:54”, “initial balance”: 1000, “training episodes”: 400000, “lookback window size”: 100, “depth”: 13, “lr”: 1e-05, “epochs”: 5, “batch size”: 32, “normalize value”: 40000, “model”: “CNN”, “comment”: “Normalized, no punish value”, “saving time”: “2021–02–23 11:44”, “Actor name”: “3263.63_Crypto_trader_Actor.h5”, “Critic name”: “3263.63_Crypto_trader_Critic.h5” }  As you can see, now depth is 13, not 30, and training took less time (42 hours). Within the same testing dataset of 3 months at the end of testing results we had 1478.50$ net worth in balance, this is quite similar to the previous tutorial. Here is a similar chart of 3 months as before:

I assume that too many indicators added to our model create noise to our training data so that our model can’t learn market price action. This is why our bot performs better with fewer indicators than more. Here is a similar comparison of our previous tutorial model with the same training and testing dataset:

As we can see, our previous model performed a little better, because it had fewer profit episodes during the testing timeframe and total net worth was also a little better.

Anyway, I think that current model with fewer indicators perform better because we used a new normalization technique where we remove trend from our data, so our model doesn’t learn to trade only Bitcoin, probably our model could even trade profitably another market pair, what we need to test of course, but this is not the subject of this tutorial.

This is how our 2021_02_21_17_54_Crypto_trader bot looks while trading on unseen data:

It’s quite impressive how it performs while there is an uptrend or when the market goes sideways. I am happy that it even learned to avoids dips. But it’s not that good when the short-term trend is going down, but the reason might be because Bitcoin, looking at the long term, mostly goes only to up direction.

Conclusion:

What we learned from this tutorial is, that more technical indicators don’t mean that they always will give us a better performance of our RL Bitcoin trading bot, this is because most of the indicators are lagging and they add too much noise to our training dataset.

Looking at the bigger picture of what we achieved from developing random trading agent in my first tutorial and seeing it now, it’s quite impressive results. Speaking openly, while doing this tutorial I didn’t even expect that it’s possible to beat the market and make this kind of automation bot do trades profitably.

Of course, this bot is not perfect, experienced automated bot coders might give me a lot of advice what where and how I could improve it, but because I was developing and researching this by myself in my spare time, results exceeded my expectations!

This tutorial code can be copied and used where ever you want, but I do not recommend using it for real trading, because you might lose all your money, don’t blame me for that if you do so! Anyway, this whole stuff with reinforcement learning required too much effort, time and knowledge, this means that this is my last Bitcoin trading tutorial part and I might not come back to it quite soon. I never say never, but I still need more Reinforcement Learning and deeper Machine Learning knowledge to achieve better results!

For now, I’ll continue researching other Python and Machine learning spheres, if you interested you can follow me.

Thanks for reading! As always, all the code given in this tutorial can be found on my GitHub page and is free to use!

All of these tutorials are for educational purposes, and should not be taken as trading advice. You should not trade based on any algorithms or strategies defined in this, previous, or future tutorials, as you are likely to lose your investment.