This financial API warns you before stocks crash! | by Pranjal Saxena | May, 2024

The Financial Modeling Prep (FMP) API is a versatile tool in the financial sector. It provides users with a wealth of real-time financial data. This data is essential for effective market analysis.

With the FMP API, users have access to a range of financial indicators. These include income statements, balance sheets and market performance measures. Such comprehensive data helps in detailed and accurate analysis.

The benefit of using recent data cannot be overstated. This ensures that investors make decisions based on the most current market conditions. This timeliness is crucial in volatile markets where conditions change rapidly.

Access to up-to-date information enables investors to react more quickly to market changes. This responsiveness can be the difference between taking advantage of opportunities and sustaining losses.

In volatile markets, the FMP API’s ability to provide real-time data becomes incredibly valuable. It helps investors avoid common financial pitfalls by providing insights that guide safer, more strategic investment decisions.

Next, we’ll explore the methodology behind using this API to predict inventory crashes. This includes how to choose the right data and indicators for effective market analysis.

To effectively predict stock crashes, a structured approach centered around careful data collection is essential. The Financial Modeling Prep (FMP) API facilitates access to extensive historical data that serves as the foundation for any robust analytical model.

Data Collection

Initially, investors use the FMP API to extract historical price data, trading volumes and market capitalization details. This comprehensive data set is essential for performing deep trend analysis. Automating data retrieval through Python scripts ensures continuous, real-time data updates, which is essential to keep the analysis relevant and timely.

Indicator selection

The next step is the careful selection of technical indicators that are known to indicate potential market downturns. These include moving averages (MA), relative strength index (RSI) and moving average convergence divergence (MACD). Each of these indicators plays a crucial role in the technical analysis framework:

Moving averages help smooth price data to create a single flowing line, making it easier to identify the direction of the trend. The crossing of short-term and long-term moving averages often indicates a potential price reversal, which is a strong market signal. Relative Strength Index (RSI) is used to determine overbought or oversold conditions in the market. An RSI level above 70 indicates that a stock may be overbought, while an RSI below 30 indicates an oversold state. These thresholds help analysts anticipate potential reversals in market trends. Moving average convergence divergence (MACD) is crucial for detecting changes in the momentum of stock prices, helping to identify upcoming trend reversals before they occur.

Analysis techniques

Applying these indicators to the historical data collected from the FMP API involves a detailed examination of patterns that may precede an accident. Analysts will:

Apply Mathematical Formulas To integrate these indicators, mathematical formulas such as the calculation of EMA (Exponential Moving Average) or smoothing techniques for RSI will be used to refine the raw data into actionable insights. Graph visualization The use of graphical representations is integral in explaining what numerical and formulaic indicators can be unclear. Charts such as candlestick patterns, line charts for moving averages, and histogram plots for MACD provide visual contexts that are more intuitive for spotting trends. Convergence and divergence analysis This involves looking for cases where the behavior of price and indicators diverge from each other, signaling possible future movements. For example, if price makes new highs while indicators such as MACD are falling, this may suggest an upcoming reversal.

In the following sections, the article will delve into Python implementations that bring these theoretical concepts into practical application. It will include specific code examples that demonstrate how to apply these indicators to real-world data, thereby enabling investors to effectively perform their own predictive analytics.

This section delves into the Python implementation needed to apply the theoretical frameworks discussed earlier. We will explore how to retrieve, process and analyze data with Python, focusing on key technical indicators.

Retrieve and process data

The first step in our analysis involves retrieving historical inventory data using the `fetch_data` function. This function is crucial for accessing up-to-date and relevant financial data through the FMP API. Here’s a basic breakdown of how this feature works:

import requests from requests.exceptions import HTTPError, Time time import pandas as pdiimport numpy as npfrom scipy.signal import argrelextremaimport time

def fetch_data(symbol, api_key):url = f”https://financialmodelingprep.com/api/v3/historical-chart/1min/{symbol}?apikey={api_key}”try:response = requests.get(url, time-out=10)response.raise_for_status()data = response.json()returndata except HTTPError if http_err:print(f”HTTP error occurred: {http_err}”)except Timeout if timeout_err:print(f” Request timed out: {timeout_err) }”) except Exception if e:print(f”Other error occurred: {e}”)return None

This feature includes robust error handling to ensure that data recovery is reliable and the system remains robust against common network issues.

Next, the `convert_to_dataframe` function converts raw JSON data into a cleaned and structured pandas DataFrame. This is essential for the subsequent technical analysis:

def convert_to_dataframe(data):if not data:return pd.DataFrame()return (pd.DataFrame(data).assign(date=lambda df: pd.to_datetime(df)[‘date’]).dt.tz_localize(‘UTC’)).set_index(‘date’).apply(pd.to_numeric, errors=’coerce’).resample(‘5T’).agg({‘open’: ‘first’, ‘high’: ‘max’, ‘low’: ‘min’, ‘close’: ‘last’}).dropna())

api_key = ‘YOUR FMP API KEY’ # Replace with your actual API key symbol = ‘AAPL’

raw_data = fetch_data(symbol, api_key) dataframe = convert_to_dataframe(raw_data)

Application of Technical Indicators

With the data prepared, we apply technical indicators such as moving averages, RSI and MACD. These indicators are crucial for identifying potential price movements before they occur:

You can use below python command in colab notebook to install TA-lib library:

!wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz!tar -xzvf ta-lib-0.4.0-src.tar.gz%cd ta- lib!./configure –prefix=/usr!make!make install

!pip installs TA-Lib

Here is how we can calculate different indicators:

from talib import RSI, MACD, SMA

def application_indicators ( dataFrame ): dataFrame[‘SMA_30’] = SMA(dataframe[‘close’]time period=30) dataframe[‘RSI’] = RSI(dataframe[‘close’]time period=14) dataframe[‘macd’]data frame[‘macdsignal’]data frame[‘macdhist’] = MACD(dataframe[‘close’]fast period=12, slow period=26, signal period=9) return dataframe

dataframe = apply_indicators(dataframe)

This code integrates widely used technical analysis libraries to add key trading indicators directly to our DataFrame.

Visual Analysis

Visual interpretation of these indicators is performed using plotting tools such as Matplotlib and Seaborn. These visualizations play a critical role in clarifying the signals suggested by our technical analysis:

import matplotlib.pyplot as pltimport seaborn as sns

def plot_indicators(dataframe): plt.figure(figsize=(14, 7))plt.plot(dataframe[‘close’]label=’Close price’) plt.plot(dataframe[‘SMA_30′]label=’30-Day SMA’)plt.title(‘Price Trends and Moving Averages’)plt.legend()plt.show()# RSI plotplt.figure(figsize=(14, 5))plt.plot( dataframe[‘RSI’]label=’RSI’)plt.title(‘Relative Strength Index’)plt.legend()plt.show()

plot_indicators (dataframe)

These plots help investors to visually determine the conditions that may precede a stock decline, and provide a clear picture of when to potentially enter or exit the market based on predetermined thresholds.

After this comprehensive examination of Python implementation and technical analysis, the next section will provide a detailed exploration of creating and interpreting charts representing these indicators. This graphical analysis is the key to making informed decisions in stock trading.

Visual representations play a crucial role in financial analysis, especially when it comes to understanding complex market data. This section outlines how to create and interpret various graphs that visualize financial trends and indicators with Python.

For starters, using Python libraries like Matplotlib and Seaborn eases the process of mapping financial data. These tools can transform raw data into informative visual representations. Here’s how you can plot the closing prices along moving averages:

import matplotlib.pyplot as plt

def plot_close_prices_and_SMA(dataframe):plt.figure(figsize=(10, 5))plt.plot(dataframe.index, dataframe[‘close’]label=’Close price’)plt.plot(dataframe.index, dataframe[‘SMA_30′]label=’30-Day SMA’, linestyle=’ – ‘)plt.title(‘Close prices and moving averages’)plt.xlabel(‘Date’)plt.ylabel(‘Price’)plt.legend()plt . Show()

This chart provides a clear picture of how the stock price interacts with its 30-day moving average, a key indicator of potential market movements.

Another useful visualization is the MACD chart, which is critical for detecting trend reversals:

def plot_MACD(dataframe):plt.figure(figsize=(10, 5))plt.plot(dataframe.index, dataframe[‘macd’]label=’MACD Line’)plt.plot(dataframe.index, dataframe[‘macdsignal’]label=’Signal line’, linestyle=’ – ‘)plt.bar(dataframe.index, dataframe[‘macdhist’]label=’MACD Histogram’, color=’grey’)plt.title(‘MACD’)plt.xlabel(‘Date’)plt.ylabel(‘Value’)plt.legend()plt.show()

Interpretation of graphs

Interpreting these graphs is key to effectively harnessing their insights. For example, in the SMA chart, a price crossing above the SMA can indicate a bullish trend, suggesting a potential buying opportunity. Conversely, a price drop below the SMA can indicate a bearish trend, prompting a sell or hold strategy.

The MACD chart provides insight into the momentum behind price changes. A crossing of the MACD line above the signal line indicates increasing bullish momentum, possibly indicating an upcoming rise in stock price. If the MACD crosses below, it may indicate growing bearish momentum, warning of a possible price decline.

Additionally, candlestick charts can be invaluable in visualizing price action more dynamically. They provide a detailed overview of open, high, low and close prices within a specific time frame. Recognizing patterns in these charts can help predict short-term movements in stock prices.

Application in Predictive Analytics

These visual tools are not only for retrospective analysis but are integral in predictive scenarios. By regularly updating and reviewing these charts, investors can stay ahead of market trends. This ability to anticipate and respond to market conditions is crucial to avoiding financial pitfalls.

Finally, mastering these visualization techniques provides investors with a clearer perspective on market dynamics. The next section will summarize the benefits of integrating the FMP API with Python for effective stock market analysis. This synthesis of tools and techniques promotes a protective strategy against volatile market swings.