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# Tom Demark’s Rate of Change Indicator for Trading

### Creating and Coding the TD Rate of Change Indicator in Python

Tom Demark has created many indicators, among them his own rate of change indicator which is a contrarian indicator based on momentum.

I have released a new book after the success of my previous one “*Trend Following Strategies in Python*”. It features advanced contrarian indicators and strategies with a **GitHub **page dedicated to the continuously updated code. If you feel that this interests you, feel free to visit the below Amazon link (which contains a sample), or if you prefer to buy the PDF version, you could check the link at the end of the article.

**Contrarian Trading Strategies in Python***Amazon.com: Contrarian Trading Strategies in Python: 9798434008075: Kaabar, Sofien: Books*www.amazon.com

### The Normal 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.

To code the Rate of Change Indicator in Python, we can use the following function:

`def roc(Data, lookback, what, where):`

`for i in range(len(Data)):`

```
Data[i, where] = ((Data[i, what] - Data[i - lookback, what]) / Data[i - lookback, what]) * 100
return Data
```

To be able to manipulate the data, we first need to have an OHLC array (not a data frame) and define the following three small manipulation functions:

**# The function to add a certain number of columns**
def adder(Data, times):
for i in range(1, times + 1):
z = np.zeros((len(Data), 1), dtype = float)
Data = np.append(Data, z, axis = 1)

`return Data`

**# The function to deleter a certain number of columns**
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 certain number of rows from the beginning**
def jump(Data, jump):
Data = Data[jump:, ]
return Data

### Tom Demark’s Rate of Change Indicator

The version that Tom Demark has created uses a different lookback period and resembles the Momentum Indicator more than the Rate-of-Change Indicator. The formula for the TD Rate-of-Change is the following:

```
def td_roc_indicator(Data, what, where):
for i in range(len(Data)):
```

```
Data[i, where] = Data[i, what] / Data[i - 12, what] * 100
Data = jump(Data, lookback)
return Data
```

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### Summary

To sum up, what I am trying to do is to simply contribute to the world of objective technical analysis which is promoting more transparent techniques and strategies that need to be back-tested before being implemented. This way, technical analysis will get rid of the bad reputation of being subjective and scientifically unfounded.

I recommend you always follow the the below steps whenever you come across a trading technique or strategy:

*Have a critical mindset and get rid of any emotions.**Back-test it using real life simulation and conditions.**If you find potential, try optimizing it and running a forward test.**Always include transaction costs and any slippage simulation in your tests.**Always include risk management and position sizing in your tests.*

Finally, even after making sure of the above, stay careful and monitor the strategy because market dynamics may shift and make the strategy unprofitable.