Chapter 2: Understanding Moving Averages

Moving averages serve as a means to confirm and leverage market trends. Their popularity as a technical indicator stems from their simplicity and effectiveness in enhancing analysis. These tools can assist in pinpointing support and resistance levels, setting stop-loss orders, and comprehending the overall market trend. Their versatility renders them an essential component of any trader’s toolkit.

For instance, the 200-period simple moving average of EUR/USD demonstrates how moving averages can provide dynamic support and resistance levels, allowing traders to make informed decisions regarding order placements.

Mathematically, the moving average is the sum of observations divided by the total number of observations. Its formula is as follows:

def adder(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 deleter(Data, index, times):

for i in range(1, times + 1):

Data = np.delete(Data, index, axis=1)

return Data

def jump(Data, jump):

Data = Data[jump:, ]

return Data

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

return Data

my_data = ma(my_data, 200, 3, 4)

Chapter 3: Creating the Global Moving Average Scanner

The Global Moving Average Scanner acts as an index comprising various moving averages. Its primary function is to illustrate how many moving averages lie above or below the current market price. A robust bullish trend is indicated when the market price exceeds its moving averages, whereas a strong bearish trend is signified when the market price falls below them.

We will implement a loop to compute moving averages ranging from 5 to 1000 periods, incrementing by 10.

# Adding one column for the GMAS

my_data = adder(my_data, 1)

# Where the first MA column will be

where = 4

# Looping around the data and creating moving averages

for i in range(5, 1000, 10):

my_data = ma(my_data, i, 3, where)

where = where + 1

# Cleaning the first NaN values

my_data = jump(my_data, 1000)

For instance, a value of 67 indicates that 67 out of 100 moving averages are positioned below the market, suggesting a bullish trend. This indicator, while simplistic, serves to gauge the direction and strength of the trend, revealing critical insights:

• A reading of 100 implies a strong bullish trend.
• A value surpassing 50 suggests bullish confirmation.
• A reading dropping below 50 indicates potential bearish confirmation.
• A value of 0 indicates a strong bearish trend.

The indicator should be utilized alongside other tools. For instance, a short-term RSI can be employed to monitor market drops while the Global Moving Average Index is at zero or crossing the 50 threshold.

To enhance visual interpretation, we can color-code the bars: green for values above 50, indicating a bullish state, and red for values below 50. Below is a function to implement this:

def ohlc_plot_bars(Data, window):

Chosen = Data[-window:, ]

for i in range(len(Chosen)):

plt.vlines(x=i, ymin=Chosen[i, 2], ymax=Chosen[i, 1], color='white', linewidth=1)

if Chosen[i, 4] > 50:

color_chosen = 'green'

elif Chosen[i, 4] < 50:

color_chosen = 'red'

else:

color_chosen = 'blue'

plt.vlines(x=i, ymin=Chosen[i, 3], ymax=Chosen[i, 0], color=color_chosen, linewidth=1.00)

plt.grid()

Chapter 4: Conclusion and Future Prospects

Although moving averages are inherently lagging indicators, employing multiple averages can provide valuable confirmation. The good news is that by adjusting the lookback periods to fit your trading style, such as {20, 30, 60, 100, 200, 300, 500}, you can optimize this approach to better suit your strategy.

If you're intrigued by additional technical indicators and strategies, my latest book could be of interest.

Chapter 5: Supporting Humanitarian Causes Through NFTs

I have recently launched an NFT collection aimed at supporting various humanitarian and medical initiatives. The Society of Light is a series of limited collectibles designed to contribute positively to the world, with a percentage of proceeds directed towards the associated charity for each avatar. Here are some advantages of purchasing these NFTs:

• High-potential gain: By focusing the remaining sales proceeds on marketing and promoting The Society of Light, I aim to maximize their value in the secondary market. Remember, trading in this market also means a portion of royalties will benefit the same charity.
• Art collection and portfolio diversification: Owning a collection of avatars that embody goodwill is genuinely fulfilling. Investing can be about more than just personal gain; it can also encompass helping others and appreciating art.
• Flexibility in donations: This model allows for varying allocations to your favorite charities.
• Free copy of my book: Any NFT buyer will receive a complimentary PDF version of my latest book.

Explore the following videos for deeper insights into market trends:

The first video titled "How Triple Moving Averages Help Classify Market Regimes" discusses how combining moving averages can provide clarity on market phases.

The second video titled "How Algorithmic Traders Can Identify Market Trends" showcases how traders can utilize moving averages to spot emerging trends.