The Heatmap Technical Indicator.
Creating the Heatmap Technical Indicator in Python.
Heatmaps offer a quick and clear view of the current situation. In trading, we can use them with correlations but we can also use them to detect imminent reactions or to confirm the underlying trend. This article discusses the creation of the heatmap on the values of the RSI.
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The Market Regime
The market’s regime is its current state and can be divided into:
Bullish trend: The market has a tendency to make higher highs meaning that the aggregate direction is upwards.
Sideways: The market has a tendency to to range while remaining within established zones.
Bearish trend: The market has a tendency to make lower lows meaning that the aggregate direction is downwards.
Many tools attempt to detect the trend and most of them do give the answer but we can not really say we can predict the next state. The best way to solve this issue is to assume that the current state will continue and trade any reactions, preferably in the direction of the trend. For example, if the EURUSD is above its moving average and shaping higher highs, then it makes sense to wait for dips before buying and assuming that the bullish state will continue, also known as a trend-following strategy.
The Relative Strength Index
First introduced by J. Welles Wilder Jr., the RSI is one of the most popular and versatile technical indicators. Mainly used as a contrarian indicator where extreme values signal a reaction that can be exploited. Typically, we use the following steps to calculate the default RSI:
Calculate the change in the closing prices from the previous ones.
Separate the positive net changes from the negative net changes.
Calculate a smoothed moving average on the positive net changes and on the absolute values of the negative net changes.
Divide the smoothed positive changes by the smoothed negative changes. We will refer to this calculation as the Relative Strength — RS.
Apply the normalization formula shown below for every time step to get the RSI.
The above chart shows the hourly values of the GBPUSD in black with the 13-period RSI. We can generally note that the RSI tends to bounce close to 25 while it tends to pause around 75. To code the RSI in Python, we need an OHLC array composed of four columns that cover open, high, low, and close prices.
def adder(data, times):
for i in range(1, times + 1):
new = np.zeros((len(data), 1), dtype = float)
data = np.append(data, new, axis = 1)return datadef deleter(data, index, times):
for i in range(1, times + 1):
data = np.delete(data, index, axis = 1)return data
def jump(data, jump):
data = data[jump:, ]
return datadef ma(data, lookback, close, where):
data = adder(data, 1)
for i in range(len(data)):
try:
data[i, where] = (data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
data = jump(data, lookback)
return datadef ema(data, alpha, lookback, what, where):
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, what, where)data[lookback + 1, where] = (data[lookback + 1, what] * alpha) + (data[lookback, where] * beta)for i in range(lookback + 2, len(data)):
try:
data[i, where] = (data[i, what] * alpha) + (data[i - 1, where] * beta)
except IndexError:
pass
return datadef rsi(data, lookback, close, where):
data = adder(data, 5)
for i in range(len(data)):
data[i, where] = data[i, close] - data[i - 1, close]
for i in range(len(data)):
if data[i, where] > 0:
data[i, where + 1] = data[i, where]
elif data[i, where] < 0:
data[i, where + 2] = abs(data[i, where])
lookback = (lookback * 2) - 1 # From exponential to smoothed
data = ema(data, 2, lookback, where + 1, where + 3)
data = ema(data, 2, lookback, where + 2, where + 4)data[:, where + 5] = data[:, where + 3] / data[:, where + 4]
data[:, where + 6] = (100 - (100 / (1 + data[:, where + 5]))) data = deleter(data, where, 6)
data = jump(data, lookback) return dataIf you are also interested by more technical indicators and strategies, then my book might interest you:
Detecting the Trend Using the Heatmap
The concept of detecting the trend using the RSI is not very useful but is very easy to see. Whenever the RSI is showing a reading that is greater than 50, a bullish regime is in-progress and whenever it is showing a reading that is less than 50, a bearish regime is in-progress. Therefore, we can create the conditions below by tracing a vertical colored line accordingly. Here is how:
A green vertical line is traced when the RSI is above 50.
A red vertical line is traced when the RSI is below 50.
By zooming out, we will get a basic form of heatmap as shown in the below chart.
The full code for the plot is as follows:
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
if sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
ax[1].grid()indicator_plot(my_data, 4, window = 500)
Remember to call the RSI function on the fifth column of your OHLC array. The fifth column is indexed at 4. The lookback parameters must be tweaked to reduce lag and false signals. Also, we can use other indicators and conditions.
Another Idea: Creating the RSI Heatmap for Extreme Conditions
The RSI is better known as a contrarian indicator. This means that we can apply the following rules:
Whenever the RSI is approaching the upper values, the color approaches red.
Whenever the RSI is approaching the lower values, the color approaches green.
By zooming out, we will get a basic form of heatmap as shown in the below chart.
The full code for the plot is as follows:
import matplotlib.pyplot as pltdef indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
if sample[i, second_panel] > 80 and sample[i, second_panel] < 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5)
if sample[i, second_panel] > 70 and sample[i, second_panel] < 80:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5)
if sample[i, second_panel] > 60 and sample[i, second_panel] < 70:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5)
if sample[i, second_panel] > 50 and sample[i, second_panel] < 60:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 40 and sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 30 and sample[i, second_panel] < 40:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5)
if sample[i, second_panel] > 20 and sample[i, second_panel] < 30:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5)
if sample[i, second_panel] > 10 and sample[i, second_panel] < 20:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5)
if sample[i, second_panel] > 0 and sample[i, second_panel] < 10:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
ax[1].grid()indicator_plot(my_data, 4, window = 500)
Dark green areas signify an imminent bullish reaction and dark red areas signify an imminent bearish reaction. The greyish zone is whenever the RSI is around 50.
Conclusion
Remember to always do your back-tests. You should always believe that other people are wrong. My indicators and style of trading may work for me but maybe not for you.
I am a firm believer of not spoon-feeding. I have learnt by doing and not by copying. You should get the idea, the function, the intuition, the conditions of the strategy, and then elaborate (an even better) one yourself so that you back-test and improve it before deciding to take it live or to eliminate it. My choice of not providing specific Back-testing results should lead the reader to explore more herself the strategy and work on it more.
To sum up, are the strategies I provide realistic? Yes, but only by optimizing the environment (robust algorithm, low costs, honest broker, proper risk management, and order management). Are the strategies provided only for the sole use of trading? No, it is to stimulate brainstorming and getting more trading ideas as we are all sick of hearing about an oversold RSI as a reason to go short or a resistance being surpassed as a reason to go long. I am trying to introduce a new field called Objective Technical Analysis where we use hard data to judge our techniques rather than rely on outdated classical methods.