def analyze(df_value):
from DataPrepare import prepare_data
# Getting the numpy ndarray of close prices.
na_value = df_value.values
# Normalizing the prices to start at 1 and see relative returns
na_normalized_value = na_value / na_value[0, :]
# Copy the normalized prices to a new ndarry to find returns.
na_rets = na_normalized_value.copy()
# Calculate the daily returns of the prices. (Inplace calculation)
# returnize0 works on ndarray and not dataframes.
tsu.returnize0(na_rets)
na_port_total = np.cumprod(na_rets + 1)
daily_port_ret = np.average(na_rets)
port_cum_ret = na_port_total[-1]
port_vol = np.std(na_rets)
port_sharpe = (daily_port_ret/port_vol)*np.sqrt(252)
ls_date = df_value.index.tolist()
dt_start = ls_date[0]
dt_end = ls_date[-1]
ls_keys = ["close"]
ls_symbols = ["$SPX"]
d_data = prepare_data(dt_start, dt_end, ls_keys, ls_symbols)
# Getting the numpy ndarray of close prices.
na_price = d_data['close'].values
# Normalizing the prices to start at 1 and see relative returns
na_normalized_price = na_price / na_price[0, :]
# Copy the normalized prices to a new ndarry to find returns.
na_market_rets = na_normalized_price.copy()
# Calculate the daily returns of the prices. (Inplace calculation)
# returnize0 works on ndarray and not dataframes.
tsu.returnize0(na_market_rets)
na_market_total = np.cumprod(na_market_rets + 1)
daily_market_ret = np.average(na_market_rets)
market_cum_ret = na_market_total[-1]
market_vol = np.std(na_market_rets)
market_sharpe = (daily_market_ret/market_vol)*np.sqrt(252)
print "The final value of the portfolio using the sample file is", dt_end, " ", df_value['Portfolio_value'][-1]
print ""
print "Details of the Performance of the portfolio :"
print ""
print "Data Range :", dt_start, " to ", dt_end
print ""
print "Sharpe Ratio of Fund :", port_sharpe
print "Sharpe Ratio of $SPX :", market_sharpe
print ""
print "Total Return of Fund :", port_cum_ret
print "Total Return of $SPX :", market_cum_ret
print ""
print "Standard Deviation of Fund :", port_vol
print "Standard Deviation of $SPX :", market_vol
print ""
print "Average Daily Return of Fund :", daily_port_ret
print "Average Daily Return of $SPX :", daily_market_ret
ls_symbols = ["Portfolio", "SP500"]
na_port_total = np.reshape(na_port_total,(len(na_port_total),1))
na_final = np.hstack((na_port_total, na_normalized_price))
ldt_timestamps = df_value.index.tolist()
plt.clf()
plt.plot(ldt_timestamps, na_final)
plt.legend(ls_symbols)
plt.ylabel('Portfolio Normalized Value')
plt.xlabel('Date')
plt.savefig('simulated_portfolio.pdf', format='pdf')
ax[0].plot(ldt_timestamps, na_final)
ax[0].legend(['up','bottom', 'price'])
ax[1].plot(ldt_timestamps, f_bollinger)
ax[1].axhline(y=1, color='b')
ax[1].axhline(y=-1, color='b')
ax[1].vlines(x = ldt_timestamps[np.where(f_bollinger > 1)], ymin = -3, ymax = 4, color = 'r')
ax[1].vlines(x = ldt_timestamps[np.where(f_bollinger < -1)], ymin = -3, ymax = 4, color = 'green')
ax[1].fill_between(ldt_timestamps, 1, -1, facecolor = 'blue', alpha = 0.1)
plt.title(s_sym)
df_bollinger[s_sym] = f_bollinger
return df_bollinger
if __name__ == '__main__':
# List of symbols
ls_symbols = ['MSFT','GOOG']
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Start and End date of the charts
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
d_data = prepare_data(dt_start, dt_end, ls_keys, ls_symbols)
df_bollinger = BollingerBands(d_data)
