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Udacity: Machine Learning for Trading
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| # Working with multiple stocks | |
| """ | |
| SPY is used for reference - it's the market | |
| Normalize by the first day's price to plot on "equal footing" | |
| """ | |
| import os | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| def symbol_to_path(symbol, base_dir="data"): | |
| """Return CSV file path given ticker symbol.""" | |
| return os.path.join(base_dir, "{}.csv".format(str(symbol))) | |
| def get_data(symbols, dates): | |
| """Read stock data (adjusted close) for given symbols from CSV files.""" | |
| df = pd.DataFrame(index=dates) | |
| if 'SPY' not in symbols: # add SPY for reference, if absent | |
| symbols.insert(0, 'SPY') | |
| for symbol in symbols: | |
| df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date', | |
| parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan']) | |
| df_temp.rename(columns={'Adj Close': symbol}, inplace=True) | |
| df = df.join(df_temp) | |
| if symbol == 'SPY': # drop dates SPY did not trade | |
| df = df.dropna(subset=["SPY"]) | |
| return df | |
| def normalize_data(df): | |
| """Normalize stock prices using the first row of the dataframe.""" | |
| return df / df.ix[0, :] | |
| def plot_data(df, title="Stock prices"): | |
| """Plot stock prices with a custom title and meaningful axis labels.""" | |
| ax = df.plot(title=title, fontsize=12) | |
| ax.set_xlabel("Date") | |
| ax.set_ylabel("Price") | |
| plt.show() | |
| def plot_selected(df, columns, start_index, end_index): | |
| """Plot the desired columns over index values in the given range.""" | |
| df = normalize_data(df) | |
| plot_data(df.ix[start_index:end_index, columns]) | |
| def test_run(): | |
| # Define a date range | |
| dates = pd.date_range('2010-01-01', '2010-12-31') | |
| # Choose stock symbols to read | |
| symbols = ['GOOG', 'IBM', 'GLD'] # SPY will be added in get_data() | |
| # Get stock data | |
| df = get_data(symbols, dates) | |
| # Slice and plot | |
| plot_selected(df, ['SPY', 'IBM'], '2010-03-01', '2010-04-01') | |
| if __name__ == "__main__": | |
| test_run() |
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| # Timing Python operations | |
| import time | |
| t1 = time.time() | |
| print 'Execute your function' | |
| t2 = time.time() | |
| print 'The time taken by print statement is {} seconds'.format(t2-t1) |
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| """Bollinger Bands.""" | |
| import os | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| def symbol_to_path(symbol, base_dir="data"): | |
| """Return CSV file path given ticker symbol.""" | |
| return os.path.join(base_dir, "{}.csv".format(str(symbol))) | |
| def get_data(symbols, dates): | |
| """Read stock data (adjusted close) for given symbols from CSV files.""" | |
| df = pd.DataFrame(index=dates) | |
| if 'SPY' not in symbols: # add SPY for reference, if absent | |
| symbols.insert(0, 'SPY') | |
| for symbol in symbols: | |
| df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date', | |
| parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan']) | |
| df_temp = df_temp.rename(columns={'Adj Close': symbol}) | |
| df = df.join(df_temp) | |
| if symbol == 'SPY': # drop dates SPY did not trade | |
| df = df.dropna(subset=["SPY"]) | |
| return df | |
| def plot_data(df, title="Stock prices"): | |
| """Plot stock prices with a custom title and meaningful axis labels.""" | |
| ax = df.plot(title=title, fontsize=12) | |
| ax.set_xlabel("Date") | |
| ax.set_ylabel("Price") | |
| plt.show() | |
| def get_rolling_mean(values, window): | |
| """Return rolling mean of given values, using specified window size.""" | |
| return pd.rolling_mean(values, window=window) | |
| def get_rolling_std(values, window): | |
| """Return rolling standard deviation of given values, using specified window size.""" | |
| return pd.rolling_std(values, window=window) | |
| def get_bollinger_bands(rm, rstd): | |
| """Return upper and lower Bollinger Bands.""" | |
| upper_band = rm + 2*rstd | |
| lower_band = rm - 2*rstd | |
| return upper_band, lower_band | |
| def test_run(): | |
| # Read data | |
| dates = pd.date_range('2012-01-01', '2012-12-31') | |
| symbols = ['SPY'] | |
| df = get_data(symbols, dates) | |
| # Compute Bollinger Bands | |
| # 1. Compute rolling mean | |
| rm_SPY = get_rolling_mean(df['SPY'], window=20) | |
| # 2. Compute rolling standard deviation | |
| rstd_SPY = get_rolling_std(df['SPY'], window=20) | |
| # 3. Compute upper and lower bands | |
| upper_band, lower_band = get_bollinger_bands(rm_SPY, rstd_SPY) | |
| # Plot raw SPY values, rolling mean and Bollinger Bands | |
| ax = df['SPY'].plot(title="Bollinger Bands", label='SPY') | |
| rm_SPY.plot(label='Rolling mean', ax=ax) | |
| upper_band.plot(label='upper band', ax=ax) | |
| lower_band.plot(label='lower band', ax=ax) | |
| # Add axis labels and legend | |
| ax.set_xlabel("Date") | |
| ax.set_ylabel("Price") | |
| ax.legend(loc='upper left') | |
| plt.show() | |
| if __name__ == "__main__": | |
| test_run() |
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