def test_run():
in_dates = pd.date_range('2007-12-31', '2009-12-31') #Add in sample and out of sample dates
out_dates = pd.date_range('2009-12-31', '2011-12-31') #Add in sample and out of sample dates
symbols = list(['IBM'])
df = get_data(symbols, in_dates, addSPY=True)
# Compute Bollinger Bands
# 1. Compute rolling mean
rm_IBM = get_rolling_mean(df['IBM'], window=20)
# 2. Compute rolling standard deviation
rstd_IBM = get_rolling_std(df['IBM'], window=20)
# 3. Compute upper and lower bands
upper_band, lower_band = get_bollinger_bands(rm_IBM, rstd_IBM)
# 4. Add signals for buy and sell
combo_df = pd.concat([df['IBM'], rm_IBM, upper_band, lower_band], axis=1)
combo_df.columns = ['Price', 'SMA', 'upper_band', 'lower_band']
combo_df.rename(index={0:'Date'}, inplace=True)
combo_df.to_csv('IBM.csv')
#print combo_df
orders = get_bollinger_strategy(combo_df)
# Plot raw SPY values, rolling mean and Bollinger Bands
ax = df['IBM'].plot(title="Bollinger Bands", label='IBM')
rm_IBM.plot(label='SMA', color='yellow', ax=ax)
combo_df[['upper_band', 'lower_band']].plot(label='Bollinger Bands', color='cyan', ax=ax)
# Plot vertical lines for shorts
for i in range(0, len(orders)):
if (orders.ix[i]['Type'] == 'Short') and (orders.ix[i]['BB_Strat'] == 'BUY'):
ax.axvline(orders.ix[i]['Date'], color='r', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Short') and (orders.ix[i]['BB_Strat'] == 'SELL'):
ax.axvline(orders.ix[i]['Date'], color='black', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Long') and (orders.ix[i]['BB_Strat'] == 'BUY'):
ax.axvline(orders.ix[i]['Date'], color='green', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Long') and (orders.ix[i]['BB_Strat'] == 'SELL'):
ax.axvline(orders.ix[i]['Date'], color='black', linestyle='solid')
else:
pass
# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='lower left', labels=['IBM', 'SMA', 'Bollinger Bands'])
#plt.show()
fig = ax.get_figure()
fig.savefig('output/bb_strategy.png')
#prep orders for market simulator
orders['Shares'] = 100
orders.set_index('Date', inplace=True)
orders[['Symbol', 'Order', 'Shares']].to_csv('output/bb_orders.csv')
#send order to marketsims
ms.sims_output(sv=10000, of="./output/bb_orders.csv", strat_name='BB' )
def test_run():
#in and out sample dates
in_dates = pd.date_range(
'2007-12-31', '2009-12-31') #Add in sample and out of sample dates
out_dates = pd.date_range(
'2009-12-31', '2011-12-31') #Add in sample and out of sample dates
symbols = list(['IBM'])
df = get_data_more(symbols, in_dates, addSPY=True)
df_mfi = df.copy()
for col in df_mfi:
if 'SPY' in col:
try:
df_mfi.drop(col, axis=1, inplace=True)
except Exception:
pass
#COMPUTE MY STRATEGY - Money Flow Index(mfi) & RELATIVE STRENGTH (RS)
#1. Compute Typical Price
df_mfi['tprice'] = (df_mfi['IBM_High'] + df_mfi['IBM_Low'] +
df_mfi['IBM']) / 3
# 2. Raw Money Flow
df_mfi['raw_mf'] = df_mfi['tprice'] * df_mfi['IBM_Volume']
# 3. Calculate changes
df_mfi['mf_ch'] = df_mfi['raw_mf'].shift(1) - df_mfi['raw_mf']
df_mfi['mf_ch_prop'] = df_mfi['raw_mf'].shift(1) / df_mfi['raw_mf']
# 4. Compute Gain and Loss
df_mfi['mf_gain'] = df_mfi['mf_ch'].apply(lambda x: abs(x) if x > 0 else 0)
df_mfi['mf_loss'] = df_mfi['mf_ch'].apply(lambda x: abs(x) if x < 0 else 0)
# 5. Compute rolling mean Average Gain and Loss
# lower window to increase the sensitivity
df_mfi['rm_mf_gain'] = get_rolling_mean(df_mfi['mf_gain'], window=14)
df_mfi['rm_mf_loss'] = get_rolling_mean(df_mfi['mf_loss'], window=14)
# 5. Compute Money Flow Ratio
df_mfi['mf_ratio'] = df_mfi['rm_mf_gain'] / df_mfi['rm_mf_loss']
# 6. Compute mfi
df_mfi['mfi'] = df_mfi['mf_ratio'].apply(
lambda x: 100 if x == 0.0 else 100 - (100 / (x + 1)))
df_mfi.to_csv('./output/mfi_df.csv')
# 7 Add signals for buy and sell
df_mfi.rename(columns={'IBM': 'Price'}, inplace=True)
orders = get_mfi_strategy(df_mfi)
orders.to_csv('./output/mfi_orders.csv')
# #df_mfi.reset_index(inplace=True)
#
# Plot raw IBM values
ax = df_mfi['Price'].plot(title="Relative Strength Index", label='IBM')
df_mfi['mfi'].plot(label='mfi', color='black', ax=ax)
#TODO plot on secondary axis
#df_mfi[['rm_gain', 'rm_loss']].plot(label='Rolling Loss and Gain', color='cyan', ax=ax)
# Plot vertical lines for shorts
for i in range(0, len(orders)):
if (orders.ix[i]['Type'] == 'Short') and (orders.ix[i]['mfi_Strat']
== 'BUY'):
ax.axvline(orders.ix[i]['Date'], color='r', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Short') and (orders.ix[i]['mfi_Strat']
== 'SELL'):
ax.axvline(orders.ix[i]['Date'], color='black', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Long') and (orders.ix[i]['mfi_Strat']
== 'BUY'):
ax.axvline(orders.ix[i]['Date'], color='green', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Long') and (orders.ix[i]['mfi_Strat']
== 'SELL'):
ax.axvline(orders.ix[i]['Date'], color='black', linestyle='solid')
else:
pass
# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='lower left', labels=['IBM', 'MFI'])
# # #plt.show()
fig = ax.get_figure()
fig.savefig('output/mfi_strategy.png')
# #prep orders for market simulator
orders['Shares'] = 100
orders.set_index('Date', inplace=True)
orders[['Symbol', 'Order', 'Shares']].to_csv('./output/mfi_orders.csv')
# #send order to marketsims
ms.sims_output(sv=10000, of="./output/orders.csv", strat_name='mfi')
def test_run():
in_dates = pd.date_range(
'2007-12-31', '2009-12-31') #Add in sample and out of sample dates
out_dates = pd.date_range(
'2009-12-31', '2011-12-31') #Add in sample and out of sample dates
symbols = list(['IBM'])
df = get_data(symbols, out_dates, addSPY=True)
df_rsi = df.copy()
df_rsi.drop(['SPY'], axis=1, inplace=True)
#COMPUTE MY STRATEGY - RELATIVE STRENGTH INDEX (RSI) & RELATIVE STRENGTH (RS)
# 1. Compute change
df_rsi['change'] = df_rsi['IBM'].shift(1) - df_rsi['IBM']
df_rsi['change_prop'] = df_rsi['IBM'].shift(1) / df_rsi['IBM']
# 2. Compute Gain and Loss
df_rsi['gain'] = df_rsi['change'].apply(lambda x: abs(x) if x > 0 else 0)
df_rsi['loss'] = df_rsi['change'].apply(lambda x: abs(x) if x < 0 else 0)
# 3. Compute rolling mean Average Gain and Loss
# #lower window to increase the sensitivity
df_rsi['rm_gain'] = get_rolling_mean(df_rsi['gain'], window=20)
df_rsi['rm_loss'] = get_rolling_mean(df_rsi['loss'], window=20)
# 5. Compute RS
df_rsi['rs'] = df_rsi['rm_gain'] / df_rsi['rm_loss']
# 6. Compute RSI
df_rsi['rsi'] = df_rsi['rs'].apply(lambda x: 100
if x == 0.0 else 100 - (100 / (x + 1)))
# 7 Add signals for buy and sell
#df_rsi.columns =['Price', 'change', 'gain', 'loss', 'rm_gain', 'rm_loss', 'rs', 'rsi']
orders = get_rsi_strategy(df_rsi)
orders.to_csv('./output/rsi_orders (in sample).csv')
#df_rsi.reset_index(inplace=True)
# Plot raw IBM values
ax = df_rsi['IBM'].plot(title="Relative Strength Index", label='IBM')
df_rsi['rsi'].plot(label='RSI', color='black', ax=ax)
#TODO plot on secondary axis
#df_rsi[['rm_gain', 'rm_loss']].plot(label='Rolling Loss and Gain', color='cyan', ax=ax)
# Plot vertical lines for shorts
for i in range(0, len(orders)):
if (orders.ix[i]['Type'] == 'Short') and (orders.ix[i]['RSI_Strat']
== 'BUY'):
ax.axvline(orders.ix[i]['Date'], color='r', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Short') and (orders.ix[i]['RSI_Strat']
== 'SELL'):
ax.axvline(orders.ix[i]['Date'], color='black', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Long') and (orders.ix[i]['RSI_Strat']
== 'BUY'):
ax.axvline(orders.ix[i]['Date'], color='green', linestyle='solid')
elif (orders.ix[i]['Type'] == 'Long') and (orders.ix[i]['RSI_Strat']
== 'SELL'):
ax.axvline(orders.ix[i]['Date'], color='black', linestyle='solid')
else:
pass
# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='lower left', labels=['IBM', 'RSI'])
# # #plt.show()
fig = ax.get_figure()
fig.savefig('output/rsi_strategy (in sample).png')
# #prep orders for market simulator
orders['Shares'] = 100
orders.set_index('Date', inplace=True)
orders[['Symbol', 'Order', 'Shares']].to_csv('./output/orders.csv')
# #send order to marketsims
ms.sims_output(sv=10000, of="./output/orders.csv", strat_name='RSI')
sv=10000) # testing step
#df_trades.reset_index(inplace=True)
df_trades.dropna(inplace=True)
#print df_trades
if verbose: print iteration
with open('./Orders/%s_orders.csv' % symbol, 'w+') as csvfile:
fieldnames = ['Date', 'Symbol', 'Order', 'Shares']
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(fieldnames)
for i in range(0, len(df_trades)):
trade_dt = df_trades.index[i].strftime('%Y-%m-%d')
if df_trades.ix[i, 'action'] == 1:
# Date, Symbol, Order, Shares
writer.writerow([trade_dt, symbol, 'BUY', 100])
elif df_trades.ix[i, 'action'] == 2:
writer.writerow([trade_dt, symbol, 'SELL', 100])
else:
pass
# 5) Run orders through market simulators and output back testing graph
sims_output(sv=10000,
of='./Orders/%s_orders.csv' % symbol,
gen_plot=True,
symbol=symbol,
strat_name='Strategy_%s' % (symbol))
# 3) Send test and train data to model
pred_train_Y, pred_test_Y = SendtoModel(train_df=train, train_price=train_data['price_norm'],
test_df=test, test_price=test_data['price_norm'],
model='knn', symbol=sym, verbose=True, k=3)
# 4) Create orders from predictions by merging predicted Y-returns with SPY-dates
# a) in sample
returns_train_df = pd.concat([is_spy_df, pd.DataFrame(pred_train_Y, index = train.index, columns=['predY_returns'])], axis=1)
create_5day_orders(returns_train_df, sym=sym, type='insample')
#plot_strategy(price=is_df[sym], of='./Orders/%s_knn_orders_5day_insample.csv' % sym[0], name='%s_in_sample' % sym[0])
# b) out of sample
returns_test_df = pd.concat([oos_spy_df, pd.DataFrame(pred_test_Y, index = test.index, columns=['predY_returns'])], axis=1)
create_5day_orders(returns_test_df, sym=sym, type='outsample')
# plot_strategy(price=oos_df[sym], of='./Orders/%s_knn_orders_5day_outsample.csv' % sym[0], name='%s_out_sample' % sym[0])
#create_rolling_orders(pred_return_df, sym=sym) #TODO extra credit
# 5) Run orders through market simulators and output back testing graph
sims_output(sv=start_val, of='./Orders/%s_knn_orders_5day_insample.csv' % sym[0], gen_plot=True,
symbol=sym[0], strat_name='5day_KNN_%s_%s'% (sym[0], 'insample'))
# sims_output(sv=start_val, of='./Orders/%s_knn_orders_5day_outsample.csv' % sym[0], symbol=sym[0],
# gen_plot=True, strat_name='5day_KNN_%s_%s'% (sym[0], 'outsample'))