When working with pandas-ta, a financial technical analysis tool built on top of pandas, performance can become a critical issue, especially when dealing with large datasets. Fortunately, there are several methods to improve performance and speed up calculations while using this library. This article will walk you through hundreds of lines of Python code snippets and practical tips to optimize your pandas-ta workflows.
1. Efficient Data Management
Before diving into pandas-ta specific optimizations, it’s essential to ensure your data is managed efficiently.
Use the Right Data Types
Make sure that you're using the most efficient data types. For instance, if you have an integer column that will never exceed 32,767, use int16 instead of int64 to save memory.
import pandas as pd
df = pd.DataFrame({
'prices': pd.Series([10, 20, 30], dtype='int16')
})
Optimize Data Loading
Loading data efficiently can drastically improve performance. When using methods such as read_csv, specify dtypes right away.
df = pd.read_csv('data.csv', dtype={'prices': 'int16'})
2. Leverage Dividend and Filter Functions
Using built-in functions like divisors and filters can reduce overall calculation time.
Utilize in-built Indicators
Instead of manually calculating, use available pandas-ta indicators out of the box which are optimized for performance.
from pandas_ta import ema
df['ema'] = ema(df['close'], length=20)
3. Customize Calculation Lengths
Tailor the length of your calculation window to the business need which can also reduce unnecessary compute cycles.
# Example of optimizing with a moving average
from pandas_ta import sma
df['sma'] = sma(df['close'], length=50)
4. Use NumPy for Backend Computations
pandas-ta utilizes NumPy under the hood, and sometimes directly leveraging NumPy can help speed up calculations.
import numpy as np
def custom_sma(data, period=50):
return np.convolve(data, np.ones(period)/period, mode='valid')
sma_values = custom_sma(df['close'])
5. Enable Caching for Repeated Calculations
Easily cached computations can avoid recalculating when doing repetitive analysis tasks by using tools like joblib.
from joblib import Memory
memory = Memory('cache_dir', verbose=0)
@memory.cache
def calculate_indicator(df):
return ema(df['close'], length=20)
results = calculate_indicator(df)
6. Parallelize Your Calculations
Depending on the task complexity, consider parallel processing to distribute the workload.
from joblib import Parallel, delayed
results = Parallel(n_jobs=4)(delayed(calculate_indicator)(df_part) for df_part in np.array_split(df, 4))
7. Monitor Bottlenecks Regularly
Regularly profile your code to find performance bottlenecks and focus your optimization efforts there.
import cProfile
cProfile.run('calculate_indicator(df)')
By incorporating these techniques into your workflow, you should experience noticeable performance improvements when calculating financial indicators with pandas-ta. These optimizations allow for more efficient use of computational resources, enabling you to handle larger datasets with ease.
