The dynamic nature of financial markets necessitates utilizing reliable data to develop and validate trading strategies. Efficiently incorporating high-quality data within backtesting environments is crucial for traders and analysts. TraderMade APIs empower these professionals by providing precise, detailed, and comprehensive market data.
This analysis leverages TraderMade's Time Series API to obtain historical data, execute a straightforward Simple Moving Average (SMA) crossover strategy, and evaluate its historical performance.
The Simple Moving Average (SMA) Crossover Strategy is a fundamental technical analysis technique. It involves the observation of two SMAs: a short-term SMA, which exhibits higher sensitivity to price shifts, and a long-term SMA, which mitigates the impact of short-term price volatility.
A buy signal is generated when the short-term SMA surpasses the long-term SMA, signifying a potential upward trend. Conversely, a sell signal is triggered when the short-term SMA falls below the long-term SMA, indicating a potential downward trend.
Start by installing TraderMade's SDK as follows:
!pip install tradermade
We employ the installed Software Development Kit (SDK) to retrieve hourly time series data for foreign exchange (forex) pairs. The subsequent Python code exemplifies obtaining data for the EUR/USD currency pair.
import tradermade as tm import pandas as pd def fetch_forex_data(api_key, currency, start_date, end_date, interval="hourly", fields=["open", "high", "low", "close"]): # Set API key tm.set_rest_api_key(api_key) # Fetch the data data = tm.timeseries(currency=currency, start=start_date, end=end_date, interval=interval, fields=fields) # Convert data directly to DataFrame df = pd.DataFrame(data) # Convert 'date' column to datetime df["date"] = pd.to_datetime(df["date"]) # Set 'date' as the index df.set_index("date", inplace=True) return df # Adjust as needed api_key = "YOUR TRADERMADE API KEY" currency = "EURUSD" start_date = "2024-11-01-00:00" end_date = "2024-11-27-05:12" # Fetch the data and display the first few rows forex_data = fetch_forex_data(api_key, currency, start_date, end_date) forex_data = forex_data.rename(columns={"open": "Open", "high": "High", "low": "Low", "close": "Close"}) forex_data.head()
Data acquisition and preprocessing for backtesting have been successfully completed.
This section utilizes the backtesting Python library to define and evaluate our SMA crossover strategy. For those unfamiliar with the backtesting library, it is considered a prominent and robust Python framework for backtesting technical trading strategies. These strategies encompass a diverse range, including SMA crossover, RSI crossover, mean-reversal strategies, momentum strategies, and others.
import numpy as np from backtesting import Backtest, Strategy from backtesting.lib import crossover from backtesting.test import SMA # Define the SMA crossover trading strategy class SMACrossoverStrategy(Strategy): def init(self): # Calculate shorter-period SMAs for limited data price = self.data.Close self.short_sma = self.I(SMA, price, 20) # Short window self.long_sma = self.I(SMA, price, 60) # Long window def next(self): # Check for crossover signals if crossover(self.short_sma, self.long_sma): self.buy() elif crossover(self.long_sma, self.short_sma): self.sell() # Initialize and run the backtest bt = Backtest(forex_data, SMACrossoverStrategy, cash=10000, commission=.002) result = bt.run() # Display the backtest results print("Backtest Results:") print(result)
The strategy employs two moving averages: a 20-period and a 60-period SMA. A buy order is executed when the short-term SMA surpasses the long-term SMA. Conversely, a sell order is triggered when the short-term SMA falls below the long-term SMA. Within a 25-day trading period, this straightforward strategy yielded a profit of $243 through six trades.
The subsequent Python code assesses the performance of the SMA crossover strategy. SMAs facilitate the visualization of price trends and identify crossover points that generate buy/sell signals. The equity curve serves as a performance metric, illustrating the impact of these signals on portfolio growth.
By integrating both curves, traders can readily observe the correlation between crossover events and changes in portfolio value, providing crucial insights into the efficacy of the SMA crossover strategy.
Plotly is utilized to visualize the equity and SMAs curves, enabling traders to evaluate their strategy's profitability effectively.
!pip install tradermade
Successful backtesting necessitates accurate, high-frequency data, and TraderMade's APIs facilitate seamless integration. Regardless of your experience level – whether you are a novice exploring diverse strategies or an experienced analyst developing sophisticated models – the company's offerings provide the necessary tools.
Are you prepared to incorporate TraderMade's APIs into your workflow? Initiate your journey today and transform your concepts into reality.
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