Vadym Yasinskyi

WALKEN is a Solana-based fitness-oriented play-to-earn game that encourages players to adopt healthy habits through competing and monetizing their walking steps. As they state: "We would like to give people a chance to make a living by playing a game and sticking to a healthy lifestyle at the same time".

A moving average helps cut down the amount of "noise" on a price chart. Look at the direction of the moving average to get a basic idea of which way the price is moving. If it is angled up, the price is moving up (or was recently) overall; angled down, and the price is moving down overall; moving sideways, and the price is likely in a range.

A moving average can also act as support or resistance. In an uptrend, a 50-day, 100-day or 200-day moving average may act as a support level, as shown in the figure below. This is because the average acts like a floor (support), so the price bounces up off of it. In a downtrend, a moving average may act as resistance; like a ceiling, the price hits the level and then starts to drop again.

The price won't always "respect" the moving average in this way. The price may run through it slightly or stop and reverse prior to reaching it.

As a general guideline, if the price is above a moving average, the trend is up. If the price is below a moving average, the trend is down. However, moving averages can have different lengths (discussed shortly), so one MA may indicate an uptrend while another MA indicates a downtrend.

A moving average can be calculated in different ways. A five-day simple moving average (SMA) adds up the five most recent daily closing prices and divides it by five to create a new average each day. Each average is connected to the next, creating the singular flowing line.

Another popular type of moving average is the exponential moving average (EMA). The calculation is more complex, as it applies more weighting to the most recent prices. If you plot a 50-day SMA and a 50-day EMA on the same chart, you'll notice that the EMA reacts more quickly to price changes than the SMA does, due to the additional weighting on recent price data.

Charting software and trading platforms do the calculations, so no manual math is required to use a moving average.

One type of MA isn't better than another. An EMA may work better in a stock or financial market for a time, and at other times, an SMA may work better. The time frame chosen for a moving average will also play a significant role in how effective it is (regardless of type).

Common moving average lengths are 10, 20, 50, 100 and 200. These lengths can be applied to any chart time frame (one minute, daily, weekly, etc.), depending on the trader's time horizon.

The time frame or length you choose for a moving average, also called the "look back period," can play a big role in how effective it is.

An MA with a short time frame will react much quicker to price changes than an MA with a long look back period. In the figure below, the 20-day moving average more closely tracks the actual price than the 100-day moving average does.

The 20-day may be of analytical benefit to a shorter-term trader since it follows the price more closely and therefore produces less "lag" than the longer-term moving average. A 100-day MA may be more beneficial to a longer-term trader.

Lag is the time it takes for a moving average to signal a potential reversal. Recall that, as a general guideline, when the price is above a moving average, the trend is considered up. So when the price drops below that moving average, it signals a potential reversal based on that MA. A 20-day moving average will provide many more "reversal" signals than a 100-day moving average.

A moving average can be any length: 15, 28, 89, etc. Adjusting the moving average so it provides more accurate signals on historical data may help create better future signals.

Moving averages are calculated based on historical data, and nothing about the calculation is predictive in nature. Therefore, results using moving averages can be random. At times, the market seems to respect MA support/resistance and trade signals, and at other times, it shows these indicators no respect.

One major problem is that, if the price action becomes choppy, the price may swing back and forth, generating multiple trend reversal or trade signals. When this occurs, it's best to step aside or utilize another indicator to help clarify the trend. The same thing can occur with MA crossovers when the MAs get "tangled up" for a period of time, triggering multiple losing trades.

Moving averages work quite well in strong trending conditions but poorly in choppy or ranging conditions. Adjusting the time frame can remedy this problem temporarily, although at some point, these issues are likely to occur regardless of the time frame chosen for the moving average(s).

RSI is considered overbought when above 70 and oversold when below 30. These traditional levels can also be adjusted if necessary to better fit the security. For example, if a security is repeatedly reaching the overbought level of 70 you may want to adjust this level to 80.

RSI also often forms chart patterns that may not show on the underlying price chart, such as double tops and bottoms and trend lines. Also, look for support or resistance on the RSI.

In an uptrend or bull market, the RSI tends to remain in the 40 to 90 range with the 40-50 zone acting as support. During a downtrend or bear market the RSI tends to stay between the 10 to 60 range with the 50-60 zone acting as resistance. These ranges will vary depending on the RSI settings and the strength of the security’s or market’s underlying trend.

If underlying prices make a new high or low that isn't confirmed by the RSI, this divergence can signal a price reversal. If the RSI makes a lower high and then follows with a downside move below a previous low, a Top Swing Failure has occurred. If the RSI makes a higher low and then follows with an upside move above a previous high, a Bottom Swing Failure has occurred.

The RSI is a fairly simple formula, but is difficult to explain without pages of examples. Refer to Wilder's book for additional calculation information. The basic formula is:

RSI = 100 – [100 / ( 1 + (Average of Upward Price Change / Average of Downward Price Change ) ) ]

Decentralised finance (DeFi), an emerging financial technology that aims to remove intermediaries in financial transactions, has opened up multiple avenues of income for investors. Yield farming is one such investment strategy in DeFi. It involves lending or staking your cryptocurrency coins or tokens to get rewards in the form of transaction fees or interest. This is somewhat similar to earning interest from a bank account; you are technically lending money to the bank. Only yield farming can be riskier, volatile, and complicated unlike putting money in a bank.

Yield farming involves moving crypto through different marketplaces. There is also an element of yield farming where the strategy becomes less effective when more people know about it. But yield farming is currently the most significant growth driver of the DeFi sector, helping it expand from a market cap of $500 million to $10 billion in 2020 alone. Here's a primer on yield farming.

Users providing their cryptocurrencies for the functioning of the DeFi platform are known as liquidity providers (LPs). These LPs provide coins or tokens to a liquidity pool—a smart contract-based decentralised application (dApp) that contains all the funds. Once the LPs lock tokens into a liquidity fund they are awarded a fee or interest generated from the underlying DeFi platform the liquidity pool is on.

Put simply, it is an income opportunity by lending your tokens through a decentralised application (dApp). The lending happens through smart contracts with no middleman or intermediator.

The liquidity pool powers a marketplace where anyone can lend or borrow tokens. The usage of these marketplace incurs fees from the users, and the fees are used to pay liquidity providers for staking their own tokens in the pool.

Most yield farming takes place on the ethereum platform. That is why the rewards are a type of ERC-20 token.

While lenders can use the tokens as they wish, most lenders currently are speculators looking for arbitrage opportunities by cashing in on the token's fluctuations in the market.

The estimated return in the yield farming process is calculated in terms of annual percentage yield (APY). It is the rate of return that the user gains over a year. Compound interest is also factored in the APY calculation.

Cyber theft and frauds are major concerns beyond regulatory risks that most digital assets are subject to due to the lack of concrete policies regarding cryptocurrencies worldwide. All the transactions involve digital assets which use the software as storage. Hackers can be adept at finding the vulnerabilities and exploits in the software code to steal funds.

And then, there is the volatility of tokens. Cryptocurrency prices have been historically known to be volatile. The volatility can also be in short bursts, so the price of a token can surge or cash when it is locked in the liquidity pool. This could create unrealised gains or losses, and it may result in you being better off if you had kept your coins available to trade.

Smart contracts in DeFi platforms are also not as infallible as they seem. Small teams with limited budgets build many of these emerging DeFi protocols. This can increase the risk of smart contract bugs in the platform.