# The Know Sure Thing Technical Indicator — Coding in Python.

### Presenting & Coding the Know Sure Thing Indicator.

This article discusses a structured oscillator called the Know Sure Thing. It is mainly used in reversal trading strategies and to confirm the already established bias. We will also take a look on how to use it after coding it in Python.

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### The Rate of Change Indicator

The rate of change of a security can be found by using the below formula:

It describes the evolution of the value over time. If the previous price of a security was $100 and is now $110, then the rate of change will be 10%. This means that the market price increased by 10% since the previous period.

The rate of change Indicator is an oscillator that uses the above formula on a rolling basis and for a specified lookback period. A lookback period of 3 means that we will calculate the rate of change between the current price and the price 3 periods ago. The below plot shows an example of a 10-period lookback.

Let us now see how we can use this simple indicator as a main ingredient in the Know Sure Thing cooking recipe.

### The Know Sure Thing Indicator

Developed by Martin Pring, the KST uses smoothed versions of the rate of change to interpret changes in momentum. It is an unbounded oscillator that resembles the MACD and uses four rate of change calculations with different lookback periods as illustrated below:

We then take a 10-period simple moving average on the first three ROC’s and a 15-period simple moving average on the last ROC. Finally, we use the following formula to calculate the Know Sure Thing:

We then calculate a signal line which is simply a 9-period moving average on the KST.

The figure above shows the KST in blue with the signal line in red. Notice how it oscillates over the zero line and crosses its signal line from time to time.

```
# The function to add a number of columns inside an array
def adder(Data, times):
for i in range(1, times + 1):
new_col = np.zeros((len(Data), 1), dtype = float)
Data = np.append(Data, new_col, axis = 1)
return Data
```

```
# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
for i in range(1, times + 1):
Data = np.delete(Data, index, axis = 1)
return Data
# The function to delete a number of rows from the beginning
def jump(Data, jump):
Data = Data[jump:, ]
return Data
```

```
# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)
```

```
# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)
```

```
# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)
```

`# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file`

```
def 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
# Cleaning
Data = jump(Data, lookback)
return Data
```

`def know_sure_thing(Data, lookback1, lookback2, lookback3, lookback4, close, where):`

```
Data = adder(Data, 5)
# First ROC
for i in range(len(Data)):
Data[i, where] = ((Data[i, close] - Data[i - lookback1, close]) / Data[i - lookback1, close]) * 100
# Second ROC
for i in range(len(Data)):
Data[i, where + 1] = ((Data[i, close] - Data[i - lookback2, close]) / Data[i - lookback2, close]) * 100
# Third ROC
for i in range(len(Data)):
Data[i, where + 2] = ((Data[i, close] - Data[i - lookback3, close]) / Data[i - lookback3, close]) * 100
# Fourth ROC
for i in range(len(Data)):
Data[i, where + 3] = ((Data[i, close] - Data[i - lookback4, close]) / Data[i - lookback4, close]) * 100
```

```
# Smoothing
Data = ma(Data, lookback1, where, where + 4)
Data = ma(Data, lookback1, where + 1, where + 5)
Data = ma(Data, lookback1, where + 2, where + 6)
Data = ma(Data, lookback2, where + 3, where + 7)
```

```
# KST
Data[:, where + 8] = Data[:, where + 4] + \
(Data[:, where + 5] * 2) + \
(Data[:, where + 6] * 3) + \
(Data[:, where + 7] * 4)
# Signal line
Data = ma(Data, 9, where + 8, where + 9)
# Cleaning
Data = deleter(Data, where, 8)
return Data
```

Trading this indicator is based on simple crossover rules and are as follows:

**A long (Buy) whenever the KST line crosses over the KST signal line.****A short (Sell) whenever the KST line crosses under the KST signal line.**

```
def signal(Data, kst, kst_ma, buy, sell):
Data = adder(Data, 2)
for i in range(len(Data)):
if Data[i, kst] > Data[i, kst_ma] and Data[i - 1, kst] < Data[i - 1, kst_ma]:
Data[i, buy] = 1
```

```
elif Data[i, kst] < Data[i, kst_ma] and Data[i - 1, kst] > Data[i - 1, kst_ma]:
Data[i, sell] = -1
return Data
```

In general, the signals are a little late to the party and are not of high quality. The hit ratio and profitability metrics of this indicator are not impressive.

If you are also interested by more technical indicators and strategies, then my book might interest you:

### 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.**

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