To get started with algorithm trading, you will need to have a computer with a reliable Internet connection, as well as some specialized software. This software typically includes a trading platform, which is used to execute trades, as well as a programming language or application programming interface (API) that can be used to develop and test trading strategies. The execution will require instant data feeds which most brokers will charge extra for. High frequency trading is easiest to do on liquid shares and cheapest on a big stock exchange.

The most frequently used programming language for algorithm trading is probably C++, followed by Python and Java. These languages are popular among traders because they are fast, efficient, and widely used in the financial industry. C++ is known for its ability to process large amounts of data quickly, which is important for high-frequency trading. Python is a versatile language that is easy to learn and use, and is often used for developing and testing trading strategies. Java is also a popular choice among traders because of its platform-independence and reliability.

There are many different software options available for algorithm trading, so the best choice will depend on your specific needs and preferences. Some popular options include MetaTrader, TradeStation, and NinjaTrader. These platforms typically offer a range of tools and features, such as real-time market data, technical analysis indicators, and the ability to backtest and optimize trading strategies. It may be a good idea to research and compare different software options before deciding on the one that is right for you.

Here is an example of a simple scalping strategy written in Python:

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# Import necessary libraries
import pandas as pd
import numpy as np

# Load data and set index
data = pd.read_csv(‘data.csv’)
data.set_index(‘timestamp’, inplace=True)

# Define scalping strategy
def scalp(data):
# Set buy and sell thresholds
buy_threshold = 0.01
sell_threshold = -0.01

# Create empty lists to store results
buy_prices = []
sell_prices = []

# Loop through each timestamp in data
for t in range(1, len(data)):
# Get current and previous price
cur_price = data.iloc[t][‘close’]
prev_price = data.iloc[t-1][‘close’]

# Calculate price difference
diff = cur_price – prev_price

# If difference is greater than buy threshold, buy
if diff > buy_threshold:
buy_prices.append(cur_price)

# If difference is less than sell threshold, sell
elif diff < sell_threshold:
sell_prices.append(cur_price)

# Return buy and sell prices
return buy_prices, sell_prices

# Run strategy and print results
buy_prices, sell_prices = scalp(data)
print(‘Buy Prices:’, buy_prices)
print(‘Sell Prices:’, sell_prices)

This code creates a function called scalp that takes in a dataframe of market data and returns a list of buy and sell prices. The strategy buys when the price difference between the current and previous timestamps is greater than a specified threshold, and sells when the difference is less than a specified threshold. This is just a simple example to illustrate the basic structure of a scalping strategy in Python, and it can be modified and expanded upon to create a more complex and robust trading system.

Most brokers offer their own support for algo-trading. If you already use a broker, check their documentation first.