#Code to calculate max drawdown code#

#Code to calculate max drawdown code#

The last part of the code plots the candlestick chart of the identified doji candles. The code creates a new column in the dataset called 'doji' and uses the talib library to detect the doji candlestick pattern. This pattern suggests indecision in the market and can indicate a potential trend reversal. A doji candlestick pattern occurs when the opening and closing prices of a stock are very close to each other, creating a small or no real body. The second part of the code identifies the doji candlestick pattern in the data. Then it uses the Plotly library to plot the candlestick chart of the stock prices. The first part of the code displays some information about the fetched data, such as the size of the dataset and the first and last two rows of the data. The code uses a Python library called nsepy to fetch historical stock prices data of a company called HDFCAMC for the year 2021. Candlestick charts are a popular way to represent stock prices visually. This code is about analyzing the stock prices of a particular company using a candlestick chart. The code can be modified for other stocks and candlestick patterns as well. The resulting plot shows the identified Engulfing pattern on the chart. This creates a new DataFrame containing only the rows where the Engulfing pattern was identified.įinally, the plot_candle function is used to plot the candlestick chart for the Engulfing pattern data with the title as 'Engulfing Candlestick Pattern - HDFCAMC'. The code then subsets the DataFrame to only include rows where the engulf column is not zero or the next row's engulf column is not zero.

The CDLENGULFING function from talib is used to calculate the pattern and the resulting data is stored in a new column called engulf. The code then uses the talib library to identify the Engulfing candlestick pattern in the stock data. A function named plot_candle is defined which takes a DataFrame and a title as inputs and plots the candlestick chart for the provided DataFrame. Next, the plotly library is used to create a candlestick chart for the stock data. The data is then displayed using pandas library. It first uses the nsepy library to fetch historical stock data for a specific company (HDFCAMC in this case) for a particular time frame (Januto December 31, 2021). The above code is used to identify and plot a specific candlestick pattern called Engulfing for a particular stock. #stockmarket #python #finance #dataanalysis #data #pandas Finally, it uses the resample function to group the data by year and calculate the year-on-year percentage change in the Nifty index. Next, it converts the year column to a float data type so that it can be used in numerical calculations. It then adds a new column to the DataFrame to represent the year of each data point.

This code uses the pandas library in Python to read in a CSV file of stock market data, specifically the Nifty index. 📊ĭon't let data analysis intimidate you - with the right tools and a little bit of code, you can gain valuable insights into the world of finance! 💰 This means you can easily see how the Nifty index has changed from one year to the next, giving you insights into the overall trend of the stock market. This code reads in a CSV file of Nifty index data and uses pandas to group the data by year and calculate the year-on-year percentage change in the Nifty index. This Python code can help you get started! 🐍📈 Have you ever wanted to analyze stock market data, but didn't know where to start? 🤔 The maximum drawdown is calculated as the minimum of the daily drawdown over the rolling window.įinally, the daily drawdown and maximum drawdown are plotted using the matplotlib library. The daily drawdown is then calculated by dividing the current price by the rolling maximum and subtracting 1. Next, a rolling window of 24 hours (24*12 = 288 data points) is used to calculate the maximum price within that window. The data is then converted to a pandas dataframe and the 'close' column is converted to numeric. It then constructs the API URL using these parameters and retrieves the data from the API using the requests library. The code begins by setting the API key, symbol, interval, start and end dates, and order. It then calculates and plots the daily drawdown and maximum drawdown using a rolling window. This code retrieves historical price data for BTC/USD from the Twelve Data API for a given time period and interval.

Calculating the daily drawdown and max drawdown