# 2 More Useful Metrics For Algorithmic Traders, Week 4

# Introduction

As a part of our Open Crypto Data Initiative, we aim to provide traders and web3 enthusiasts with the tools to understand cryptocurrency markets and derive key insights into their mechanics. However, this data is limited by the knowledge of the traders using it, and so we’re providing a series of useful metrics and indicators that algorithmic and high-frequency traders can add to their arsenal when developing a trading strategy.

# 17. NVT Ratio

# Abstract

The Network Value to Transactions Ratio (NVT) is the blockchain parallel to Price-Earnings ratio used for valuing companies in traditional finance. The Metric was created by Willy Woo in 2017 and introduced by Chris Burniske in the same year.

The NVT Ratio is a means to quantify the total ‘superficial value’ of a network non-dimensionalised in ratio form by the total daily volume. This metric was created under the basis that there is a direct correlation between value market value of the network and the number of transactions on the network. This can be used to determine whether a network is in a ‘bubble’ or is overvalued at that instant. As Decentralised Ledgers such as Blockchains are primarily used to verify transactions using this readily available on-chain data the size of the network can be represented through the volume of transactions processed by the blockchain.

# Use Cases

There are two key use cases for NVT:

- Speculative Value
- Bubble Identification

# Speculative Value

A high NVT Ratio indicates high speculative value on the asset, typically in high growth stages of a company or protocol. Areas of significant growth in market cap are also high growth periods, when this happens the network is not being utilised and thus will result in a higher NVT Ratio.

# Bubble Identification

Predicting a bubble before the fact is rather elusive as a price explosion does not necessarily mean the asset is in a bubble. We can only determine this after the peak when the market reassesses the new valuation and we see if the price consolidates or crashes.

Similarly, NVT Ratio can not reliably determine a bubble ahead of time, but it is very useful for discerning between a crash or a consolidation after the price has peaked. It can determine this relatively quickly.

Using the NVT ratio we can detect the difference between consolidation and bubbles very visibly. If the NVT ratio stays within a normal range, we are not in bubble territory. If it climbs above the normal range, it’s a sign that the transactional activity is not sustaining the new valuation and we can expect a lengthy price correction.

-Willy Woo

NVT can be used for other assets but is essential that the network matures and is relatively stable before applying this metric. This is due to high speculation and excessive pump and dumping schemes in smaller less mature networks.

# Formula and Calculation

Quite simply the formula for calculating the NVT ratio is:

Where:

- NVT is the Network Value Transaction Ratio
- Market Cap is the total market cap in dollars, which is just a product of token price and total circulating supply volume
- Daily Transaction volume in dollars

# Python Implementation

def NVT_Ratio(Market_Cap, Daily_Tx):#Inputs: Market Cap and Daily Transactions in Dollars#Ouptputs: NVT Ratio

NVT = Market_Cap/Daily_Tx

return NVT #Example: BTC Market Cap at 1 Billion at 10 Million Transactions Per Dayprint(NVT_Ratio(10000000,100000))>>>100

# Example

Eren is a swing trader who trades Bitcoin Spots and is astonished by the rapid growth in BTC price at the moment. The Market cap presently sits at 1 Trillion USD, and that it has been rapidly rising for the last few days. However, Eren has a strong resolve and decides to do his due to diligence before buying the asset. He observes that BTC has only 5 Billion transaction volume at this time. He calculates the NVT Ratio to be 200. This is relatively high as the NVT value exceeds the nominal value of 75, this may suggest heavy speculation in the market and Eren decides to do some more research before proceeding in the trade.

# References

https://woobull.com/introducing-nvt-ratio-bitcoins-pe-ratio-use-it-to-detect-bubbles/

https://finance.yahoo.com/news/btc-eth-chain-analysis-nvt-141000791.html#:~:text=The NVT

https://dataguide.cryptoquant.com/network-indicators/nvt-ratio

# 18. The Commerce Index

# Abstract

The commerce index is a metric created by Willy Woo which aims to quantify the holding potential of a coin and determine how suitable said coin is for general commercial purpose. For a coin to be suitable it must have high liquidity and low volatility. Both the provider and receiver of the coin want these two aspects of a coin to be reasonable.

Good liquidity allows exchanging into the coin and out of it without too much loss in exchange rate (in detailed terms this is a factor of buy and sell spreads in an exchange market and the volume of buy and sell orders).

Low volatility means the price is relatively stable and will not swing wildly day to day.

This metric was created as a result of many payment coins arising in the market and a direct comparative metric for quantifying their suitability did not exist at the time.

# Use Case

The ultimate use case for the commerce index is to determine the holding capacity of a coin. It takes into account the two key needs for a trader and thus is a reasonable metric for determining usability.

If we look at the graph above, the commerce index is used to compare various coins on their feasibility for trading. As we can see BTC has the highest commerce score and is often used as it is the front runner and the most widely used crypto currency in the world. Where as a ‘meme coin’ like DOGE has a lower score.

# Formulas and Calculation

The formula for calculating the commerce index is:

Where:

- Where CI is the Commerce Index
- Daily Traded Volume is the total number of coins traded that day
- And volatility is given by:

Where $\sigma$ is standard deviation

This formula can also be written in the form

# Python Implementation

def Com_Index(DailyVolume, Volatility): #Com_Index commputes the commerce Index of an asset #Inputs: Daily Number of Coins traded and Volatility # Outputs: Commerce Index CI = DailyVolume/Volatility return CI #Example if 427k BTC is traded on coinbase with a volatility of 90 print(Com_Index(427000,90))>>> 4744.444444444444

# Example

Suppose Zeke wants to adopt a coin called ELDI for his online store but is unsure whether to use ELDI or BTC. He decides to factor in the commerce index for determining which asset to use on his platform.

- BTC has a daily trading volume of 427,000 and a currently volatility of 90
- ELDI has a daily trading volume of 50,000 and a volatility of 37.

He calculates the commerce index of both of these assets and that BTC and ELDI have respective Commerce Index values of 4744 and 1351. Although BTC is much more volatile than ELDI there is much more liquidity in the daily trading volume and therefore it is a more suitable coin according to the commerce Index.