draw.py

import pandas as pd
import matplotlib.pyplot as plt

import compute


if __name__ == '__main__':
    test_draw()


def test_draw():
    print 'pending'


def coloumn(df, title="Adj Close", columns=['Adj Close']):
    """Plot stock prices with a custom title and meaningful axis labels."""
    ax = df[columns].plot(title=title, fontsize=12)
    ax.set_xlabel("Date")
    ax.set_ylabel(title)
    plt.grid()
    plt.show()


def all_columns_for_df(df, isGridRequired=True):
    df.plot(grid=isGridRequired)
    plt.show()


def histogram_on_daily_returns(df):
    df = compute.daily_returns(df[['Adj Close']])
    mean = df['Adj Close'].mean()
    std = df['Adj Close'].std()
    df['Adj Close'].hist(bins=100)
    plt.axvline(mean)
    plt.axvline(std)
    plt.axvline(-std)
    plt.show()


def histogram_on_RSI(df):
    print 'pending'


def bollinger_bands_from_df(df, symbol='Adj Close'):
    # Compute Bollinger Bands
    # 1. Compute rolling mean
    rolling_mean = compute.rolling_std_mean(df[symbol], window=20)

    # 2. Compute rolling standard deviation
    rolling_std = compute.rolling_std(df[symbol], window=20)

    # 3. Compute upper and lower bands
    upper_band, lower_band = compute.bollinger_bands(rolling_mean, rolling_std)

    # Plot raw SPY values, rolling mean and Bollinger Bands
    ax = df[symbol].plot(title="Bollinger Bands", label=symbol)
    rolling_mean.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()


def bollinger_bands_diff_from_df(df, symbol='Adj Close'):
    # 1. Compute rolling mean
    rm_SPY = compute.rolling_std_mean(df[symbol], window=20)

    # 2. Compute rolling standard deviation
    rstd_SPY = compute.rolling_std(df[symbol], window=20)

    # 3. Compute upper and lower bands
    upper_band, lower_band = compute.bollinger_bands(rm_SPY, rstd_SPY)

    diff = upper_band - lower_band

    # Plot raw SPY values, rolling mean and Bollinger Bands
    ax = df[symbol].plot(title="Bollinger Bands", label=symbol)
    # rm_SPY.plot(label='Rolling mean', ax=ax)
    # upper_band.plot(label='upper band', ax=ax)
    # lower_band.plot(label='lower band', ax=ax)
    diff.plot(label='diff', ax=ax)

    # Add axis labels and legend
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    ax.legend(loc='upper left')
    plt.grid()
    plt.show()


def sma(df, days=21):
    df_sma = compute.running_average(df[['Adj Close']], days)
    ax = df_sma['Adj Close'].plot(title='SMA', fontsize=12, color='r')
    df['Adj Close'].plot(title='Close', ax=ax)
    ax.set_xlabel("Date")
    ax.set_ylabel('SMA')
    plt.grid()
    plt.show()
    return ax


def rsi(df, days=14):
    df_RSI = compute.RSI(df[['Adj Close']], days)
    print df_RSI.head()
    ax = df_RSI.plot(title='RSI', fontsize=12, color='r')
    # df['Adj Close'].plot(title='Close', ax=ax)
    ax.set_xlabel("Date")
    ax.set_ylabel('RSI')
    plt.axhline(y=80.0, color='b', linestyle='-')
    plt.axhline(y=20.0, color='b', linestyle='-')
    plt.grid()
    plt.show()
    return ax


def macd(df):
    df_macd = compute.MACD(df[['Close']])
    print df_macd.head()
    print df_macd.shape
    ax = df_macd['Crossover'].plot(title='Crossover', color='b')
    # df_macd['26 ema'].plot(title='MACD', fontsize=12, ax=ax, color='r')
    # df_macd['12 ema'].plot(title='12 ema', ax=ax, color='g')
    ax.set_xlabel("Date")
    ax.set_ylabel('MACD')
    plt.grid()
    plt.show()
    return ax


def strategy_results(df, title='Strategy Results'):
    # print df.tail(1)
    last_row = df.tail(1)

    result_series = last_row['Accumulated Close']/last_row['Adj Close']
    pct_change = result_series.values[0]*100
    txn_count = df['Stance'].diff().value_counts().drop([0]).sum()

    ax = df[['Adj Close', 'Accumulated Close']].plot(title=title, fontsize=12)
    ax.set_xlabel("Date")
    ax.set_ylabel('Close Prices')

    plt.text(0.05, 0.9, 'Txn. count: '+str(txn_count), fontsize=15, ha='left', va='center', transform=ax.transAxes)
    plt.text(0.05, 0.8, '% diff: '+str(pct_change), fontsize=15, ha='left', va='center', transform=ax.transAxes)

    plt.grid()
    plt.show()


    # print gain, net trades,