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Course Notes
Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! For courses that use data, the datasets will be available in the datasets folder.
# Import any packages you want to use here
import talib
import btTake Notes
Add notes here about the concepts you've learned and code cells with code you want to keep.
Add your notes here
# Calculate 12-day EMA
stock_data['EMA_12'] = talib.EMA(stock_data['Close'], 12)
# Calculate 26-day EMA
stock_data['EMA_26'] = talib.EMA(stock_data['Close'], 26)
# Plot the EMAs with price
plt.plot(stock_data['EMA_12'], label='EMA_12')
plt.plot(stock_data['EMA_26'], label='EMA_26')
plt.plot(stock_data['Close'], label='Close')
# Customize and show the plot
plt.legend()
plt.title('EMAs')
plt.show()
# Calculate the SMA
stock_data['SMA'] = talib.SMA(stock_data['Close'], 50)
# Calculate the EMA
stock_data['EMA'] = talib.EMA(stock_data['Close'], 50)
# Plot the SMA, EMA with price
plt.plot(stock_data['SMA'], label='SMA')
plt.plot(stock_data['EMA'], label='EMA')
plt.plot(stock_data['Close'], label='Close')
# Customize and show the plot
plt.legend()
plt.title('SMA vs EMA')
plt.show()# Calculate RSI with the default time period
stock_data['RSI_14'] = talib.RSI(stock_data['Close'])
# Calculate RSI with a time period of 21
stock_data['RSI_21'] = talib.RSI(stock_data['Close'], timeperiod=21)
# Print the last five rows
print(stock_data.tail())# Calculate RSI
stock_data['RSI'] = talib.RSI(stock_data['Close'])
# Create subplots
fig, (ax1, ax2) = plt.subplots(2)
# Plot RSI with the price
ax1.set_ylabel('Price')
ax1.plot(stock_data['Close'])
ax2.set_ylabel('RSI')
ax2.plot(stock_data['RSI'], color='orangered')
ax1.set_title('Price and RSI')
plt.show()# Define the Bollinger Bands with 1-sd
upper_1sd, mid_1sd, lower_1sd = talib.BBANDS(bitcoin_data['Close'],
nbdevup=2,
nbdevdn=2,
timeperiod=20)
# Plot the upper and lower Bollinger Bands
plt.plot(bitcoin_data['Close'], color='green', label='Price')
plt.plot(upper_1sd, color='tomato', label="Upper 1sd")
plt.plot(lower_1sd, color='tomato', label='Lower 1sd')
# Customize and show the plot
plt.legend(loc='upper left')
plt.title('Bollinger Bands (1sd)')
plt.show()# Define the Bollinger Bands with 2-sd
upper_2sd, mid_2sd, lower_2sd = talib.BBANDS(bitcoin_data['Close'],
nbdevup=2,
nbdevdn=2,
timeperiod=20)
# Plot the upper and lower Bollinger Bands
plt.plot(bitcoin_data['Close'], color='green', label='Price')
plt.plot(upper_2sd, color='orange', label='Upper 2sd')
plt.plot(lower_2sd, color='orange', label='Lower 2sd')
# Customize and show the plot
plt.legend(loc='upper left')
plt.title('Bollinger Bands (2sd)')
plt.show()import bt
import talib
# Calculate the SMA
sma = price_data.rolling(20).mean()
# Define the strategy
bt_strategy = bt.Strategy('AboveSMA',
[bt.algos.SelectWhere(price_data > sma),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
# Create the backtest and run it
bt_backtest = bt.Backtest(bt_strategy, price_data)
bt_result = bt.run(bt_backtest)
# Plot the backtest result
bt_result.plot(title='Backtest result')
plt.show()import bt
import talib
# Calculate the EMA
ema['Close'] = talib.EMA(price_data['Close'], timeperiod=20)
# Define the strategy
bt_strategy = bt.Strategy('AboveEMA',
[bt.algos.SelectWhere(price_data > ema),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
# Create the backtest and run it
bt_backtest = bt.Backtest(bt_strategy, price_data)
bt_result = bt.run(bt_backtest)
# Plot the backtest result
bt_result.plot(title='Backtest result')
plt.show()#TREND FOLLOWING STRATEGY (MA CROSSOVER)
# Construct the signal
signal[EMA_short > EMA_long] = 1
signal[EMA_short < EMA_long] = -1
# Merge the data
combined_df = bt.merge(signal,price_data, EMA_short,EMA_long)
combined_df.columns = ['signal', 'Price', 'EMA_short', 'EMA_long']
# Plot the signal, price and MAs
combined_df.plot(secondary_y=['signal'])
plt.show()
# Define the strategy
bt_strategy = bt.Strategy('EMA_crossover',
[bt.algos.WeighTarget(signal),
bt.algos.Rebalance()])
# Create the backtest and run it
bt_backtest = bt.Backtest(bt_strategy, price_data)
bt_result = bt.run(bt_backtest)
# Plot the backtest result
bt_result.plot(title='Backtest result')
plt.show()#MEAN REVERSION STRATEGY
import talib
# Calculate the RSI
stock_rsi = talib.RSI(price_data['Close']).to_frame()
# Create the same DataFrame structure as RSI
signal = stock_rsi.copy()
signal[stock_rsi.isnull()] = 0
# Construct the signal
signal[stock_rsi < 30] = 1
signal[stock_rsi > 70] = -1
signal[(stock_rsi <= 70) & (stock_rsi >= 30)] = 0
# Merge data into one DataFrame
combined_df = bt.merge(signal, stock_data)
combined_df.columns = ['Signal', 'Price']
# Plot the signal with price
combined_df.plot(secondary_y = ['Signal'])
# Define the strategy
bt_strategy = bt.Strategy('RSI_MeanReversion',
[bt.algos.WeighTarget(signal),
bt.algos.Rebalance()])
# Create the backtest and run it
bt_backtest = bt.Backtest(bt_strategy, price_data)
bt_result = bt.run(bt_backtest)
# Plot the backtest result
bt_result.plot(title='Backtest result')
plt.show()# ASSESING A STRATEGY
def signal_strategy(price_data, period, name):
# Calculate SMA
sma = price_data.rolling(period).mean()
# Define the signal-based Strategy
bt_strategy = bt.Strategy(name,
[bt.algos.SelectWhere(price_data > sma),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
# Return the backtest
return bt.Backtest(bt_strategy, price_data)
# Create signal strategy backtest
sma10 = signal_strategy(price_data, 10, name='SMA10')
sma30 = signal_strategy(price_data, 30, name='SMA30')
sma50 = signal_strategy(price_data, 50, name='SMA50')
# Run all backtests and plot the resutls
bt_results = bt.run(sma10,sma30,sma50)
bt_results.plot(title='Strategy optimization')
plt.show()
def buy_and_hold(price_data, name):
# Define the benchmark strategy
bt_strategy = bt.Strategy(name,
[bt.algos.RunOnce(),
bt.algos.SelectAll(),
bt.algos.WeighEqually(),
bt.algos.Rebalance()])
# Return the backtest
return bt.Backtest(bt_strategy, price_data)
# Create benchmark strategy backtest
benchmark = buy_and_hold(price_data, name='benchmark')
# Run all backtests and plot the resutls
bt_results = bt.run(sma10, sma30, sma50, benchmark)
bt_results.plot(title='Strategy benchmarking')
plt.show()