Trend-following strategies

Financial Trading in Python

Chelsea Yang

Data Science Instructor

Two types of trading strategies

Trend-following

Bet the price trend will continue in the same direction
Use trend indicators such as moving averages, ADX, etc to construct trading signals

Mean reversion

Bet the price tends to reverse back towards the mean
Use indicators such as RSI, Bollinger Bands, etc, to construct trading signals

MA crossover strategy

The trend is your friend.

Two EMA crossover:
- Long signal: the short-term EMA crosses above the long-term EMA
- Short signal: the short-term EMA crosses below the long-term EMA

Calculate the indicators

import talib
# Calculate the indicators
EMA_short = talib.EMA(price_data['Close'],
                      timeperiod=10).to_frame()
EMA_long = talib.EMA(price_data['Close'], 
                     timeperiod=40).to_frame()

Construct the signal

# Create the signal DataFrame
signal = EMA_long.copy()
signal[EMA_long.isnull()] = 0


# Construct the signal
signal[EMA_short > EMA_long] = 1

signal[EMA_short < EMA_long] = -1

Plot the signal

# Plot the signal, price and MAs
combined_df = bt.merge(signal, price_data, EMA_short, EMA_long)
combined_df.columns = ['Signal', 'Price', 'EMA_short', 'EMA_long']

combined_df.plot(secondary_y=['Signal'])

EMA_crossover signal plot

Define the strategy with the signal

# Define the strategy
bt_strategy = bt.Strategy('EMA_crossover',

                          [bt.algos.WeighTarget(signal),
                           bt.algos.Rebalance()])

Backtest the signal based strategy

# Create the backtest and run it
bt_backtest = bt.Backtest(bt_strategy, price_data)
bt_result = bt.run(bt_backtest)

Plot backtest results

# Plot the backtest result
bt_result.plot(title='Backtest result')

MA_crossover backtest result

Let's practice!

Financial Trading in Python