Section 1.1: Introduction to Financial Modeling Prep (FMP)

The Financial Modeling Prep (FMP) API offers real-time stock prices, financial statements, major index values, historical stock data, forex rates, and cryptocurrency information. This resource allows access to stock prices in real-time, with company reports available in quarterly or annual formats, extending back up to 30 years. This article will illustrate how to leverage this API for importing stock prices and comparing them to moving averages.

Section 1.2: Understanding Technical Analysis and Moving Averages

Technical analysis involves assessing and forecasting future price movements of stocks or other financial assets by examining historical price and trading volume data along with various technical indicators. The core belief is that past price and volume patterns can provide insights into future price behavior.

Moving averages are a key technical indicator in financial analysis, particularly in stock market evaluations. They smooth out price data over a specified time frame, assisting traders and analysts in recognizing trends and making educated decisions.

To compute a moving average, you add a set number of closing prices for a particular asset over a defined period and then divide the sum by the number of data points in that period. This results in a value that reflects the average closing price for that timeframe.

The primary aim of moving averages is to filter out short-term fluctuations and noise, thus making it easier to spot trends over time. A larger number of periods results in a smoother Simple Moving Average (SMA), but it may also lag more behind the actual price data.

Moving averages help in identifying trends in price movements. If the current price surpasses the moving average, it indicates a potential uptrend (bullish momentum), while a price below the moving average may suggest a downtrend (bearish momentum).

Parameters

url : str

Returns

dict

"""

response = urlopen(url, cafile=certifi.where())

data = response.read().decode("utf-8")

return json.loads(data)

You need to create an account to obtain your API key and replace it in the code above. This process is quick and straightforward.

When you invoke the Apple quote API with the following command:

price_apple = get_jsonparsed_data(apple)[0]

You will receive a dictionary that contains various updated values. Here, we focus on two specific values: price and priceAvg50, where the latter corresponds to the 50-day moving average.

Now, let's create variables to store both pieces of data for the five stocks:

price_apple = get_jsonparsed_data(apple)[0]['price']

ma_apple = get_jsonparsed_data(apple)[0]['priceAvg50']

price_msft = get_jsonparsed_data(msft)[0]['price']

ma_msft = get_jsonparsed_data(msft)[0]['priceAvg50']

price_amzn = get_jsonparsed_data(amzn)[0]['price']

ma_amzn = get_jsonparsed_data(amzn)[0]['priceAvg50']

price_nvidia = get_jsonparsed_data(nvidia)[0]['price']

ma_nvidia = get_jsonparsed_data(nvidia)[0]['priceAvg50']

price_ms = get_jsonparsed_data(ms)[0]['price']

ma_ms = get_jsonparsed_data(ms)[0]['priceAvg50']

Using a Basic Recommendation Method

With the latest stock price and moving average in hand, you can now perform a straightforward trend analysis based on these principles:

• If the current stock price exceeds the 50-day moving average, the trend is bullish.
• Conversely, if the stock price falls below the 50-day moving average, the trend is bearish.

This can be implemented in a simple function we will call technical_signal():

def technical_signal(stock, ma_stock):

if stock > ma_stock:

print('The stock is in a bullish short-term momentum')

elif stock < ma_stock:

print('The stock is in a bearish short-term momentum')

technical_signal(price_apple, ma_apple)

technical_signal(price_msft, ma_msft)

technical_signal(price_amzn, ma_amzn)

technical_signal(price_nvidia, ma_nvidia)

technical_signal(price_ms, ma_ms)

The output will reveal the trend status for each stock, indicating whether they are in a bullish or bearish momentum.

This simple scanner can assist you in swiftly identifying which markets are trending either upwards or downwards. Keep in mind that you can apply this method with various types of moving averages and also compare current prices against yearly highs and lows.

Quality of Data

Assessing the quality of data from a provider is vital for making well-informed decisions and conducting accurate analyses. Here are some steps to evaluate data quality:

1. Accuracy: Compare the provider's data with reliable sources or real-world observations for consistency.
2. Completeness: Ensure that the data encompasses all relevant variables and time frames required for your analysis.
3. Consistency: Check if the data remains stable over time and across different datasets.
4. Timeliness: Verify how often the data is updated, as up-to-date information is critical for accurate decision-making.
5. Documentation: Look for comprehensive documentation that explains the data’s structure, variables, units, and any transformations applied.
6. Provider Reputation: Research the provider's standing in the industry by reading reviews, testimonials, and assessing their customer support.

Based on my experience, FMP meets these criteria and adds significant value in terms of technical research.

This video titled "Simple Stock Recommendation System based on Technical Trading Indicators (ta) using Python & SQL" provides an overview of how to implement a stock recommendation system with Python.

In this video, "How to build a Stock Recommendation System with Python - Part I," you'll learn the foundational steps to create a stock recommendation system utilizing Python.