# The Stochastic Keltner Trading Strategy in Python

### Creating and Back-testing a Contrarian Trading Strategy in Python

This article discusses a trading strategy based on the stochastic oscillator and the Keltner channel, a known volatility indicator. The strategy’s type is contrarian and is best used in ranging markets. The second part of the article will deal with performance evaluation on a selected sample of markets.

### The Stochastic Oscillator

The stochastic oscillator is a known bounded technical indicator based on the normalization function. It traps the high, low, and close prices between 0 and 100 so as we get a glance on overstretched markets.

The stochastic oscillator (raw version) is calculated as follows:

*Subtract the current close from the lowest low during the last 14 periods. Let’s call this step one.**Subtract the highest high during the last 14 periods from he lowest low during the last 14 periods. Let’s call this step two.**Divide step one by step two and multiply by 100.*

The result is the raw version of the stochastic oscillator. The function we can use to code the stochastic oscillator given a numpy OHLC array is as follows:

```
def stochastic_oscillator(data,
lookback,
high,
low,
close,
position,
slowing = False,
smoothing = False,
slowing_period = 1,
smoothing_period = 1):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = (data[i, close] - min(data[i - lookback + 1:i + 1, low])) / (max(data[i - lookback + 1:i + 1, high]) - min(data[i - lookback + 1:i + 1, low]))
except ValueError:
pass
data[:, position] = data[:, position] * 100
if slowing == True and smoothing == False:
data = ma(data, slowing_period, position, position + 1)
if smoothing == True and slowing == False:
data = ma(data, smoothing_period, position, position + 1)
if smoothing == True and slowing == True:
data = ma(data, slowing_period, position, position + 1)
data = ma(data, smoothing_period, position + 1, position + 2)
data = delete_row(data, lookback)
return data
```

You need to define the primal function first which are needed to make the function work. They are as follows:

```
def add_column(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 data
def delete_column(data, index, times):
for i in range(1, times + 1):
data = np.delete(data, index, axis = 1)
return data
def delete_row(data, number):
data = data[number:, ]
return data
def ma(data, lookback, close, position):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = (data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
data = delete_row(data, lookback)
return data
```

Also, make sure you have an array and not a data frame as the code exclusively works with arrays. The following Figure shows an example of the 14-period stochastic oscillator.

### The Keltner Channel

The Keltner channel is a volatility bands indicator which tries to envelop the market price so as to find dynamic support and resistance levels. The steps used to calculate the Keltner channel are as follows:

*Calculate an exponential moving average on the close prices.**Calculate an average true range (ATR) using the specified lookback period.**Add step one to step two and multiply by a constant.**Subtract step one from step two and multiply by a constant.*

The function we can use to code the Keltner channel given a numpy OHLC array is as follows:

```
def keltner_channel(data, lookback, multiplier, close, position):
data = add_column(data, 2)
data = ema(data, 2, lookback, close, position)
data = atr(data, lookback, 1, 2, 3, position + 1)
data[:, position + 2] = data[:, position] + (data[:, position + 1] * multiplier)
data[:, position + 3] = data[:, position] - (data[:, position + 1] * multiplier)
data = delete_column(data, position, 2)
data = delete_row(data, lookback)
return data
```

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Two additional functions must be defined for the above code to work and they are the functions of the Keltner channnel, smoothed moving average, and exponential moving average.

```
def ema(data, alpha, lookback, close, position):
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, close, position)
data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
for i in range(lookback + 2, len(data)):
try:
data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
except IndexError:
pass
return data
def smoothed_ma(data, alpha, lookback, close, position):
lookback = (2 * lookback) - 1
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, close, position)
data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
for i in range(lookback + 2, len(data)):
try:
data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
except IndexError:
pass
return data
def atr(data, lookback, high_column, low_column, close_column, position):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = max(data[i, high_column] - data[i, low_column], abs(data[i, high_column] - data[i - 1, close_column]), abs(data[i, low_column] - data[i - 1, close_column]))
except ValueError:
pass
data[0, position] = 0
data = smoothed_ma(data, 2, lookback, position, position + 1)
data = delete_column(data, position, 1)
data = delete_row(data, lookback)
return data
```

The following Figure shows an example of the 20-period Keltner channel.

### Creating the Strategy

The strategy is simple and has the following conditions:

A bullish signal is generated whenever the 14-period stochastic oscillator is lower than 10 while the market has just surpassed the lower Keltner.

A bearish signal is generated whenever the 14-period stochastic oscillator is above 90 while the market has just broken to the downside the upper Keltner.

```
def signal(data, close_column, stochastic_column,
upper_keltner, lower_keltner, buy_column, sell_column):
data = add_column(data, 5)
for i in range(len(data)):
try:
# Bullish pattern
if data[i, stochastic_column] < lower_barrier and \
data[i, close_column] > data[i, lower_keltner] and \
data[i - 1, close_column] < data[i - 1, lower_keltner]:
data[i + 1, buy_column] = 1
# Bearish pattern
elif data[i, stochastic_column] > upper_barrier and \
data[i, close_column] < data[i, upper_keltner] and \
data[i - 1, close_column] > data[i - 1, upper_keltner]:
data[i + 1, sell_column] = -1
except IndexError:
pass
return data
```

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The following Figure shows an example of a signal chart.

The following Figure shows an example of a signal chart.

### Performance Evaluation

If we perform a simple back-test to assess the predictive power of the strategy on GBPUSD and USDCHF, we will find the following results:

The results show positive added value from the strategy and a potential predictive ability. More research is needed into how to optimize the strategy.

*If you liked this article, do not hesitate to like and comment, to further the discussion!*