def analyze(context, perf):
import matplotlib.pyplot as plt
print('the stats: {}'.format(get_pretty_stats(perf)))
# The base currency of the algo exchange
base_currency = context.exchanges.values()[0].base_currency.upper()
# Plot the portfolio value over time.
ax1 = plt.subplot(611)
perf.loc[:, 'portfolio_value'].plot(ax=ax1)
ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
# Plot the price increase or decrease over time.
ax2 = plt.subplot(612, sharex=ax1)
perf.loc[:, 'price'].plot(ax=ax2, label='Price')
ax2.set_ylabel('{asset} ({base})'.format(
asset=context.asset.symbol, base=base_currency
))
transaction_df = extract_transactions(perf)
if not transaction_df.empty:
buy_df = transaction_df[transaction_df['amount'] > 0]
sell_df = transaction_df[transaction_df['amount'] < 0]
ax2.scatter(
buy_df.index.to_pydatetime(),
perf.loc[buy_df.index, 'price'],
marker='^',
s=100,
c='green',
label=''
)
ax2.scatter(
sell_df.index.to_pydatetime(),
perf.loc[sell_df.index, 'price'],
marker='v',
s=100,
c='red',
label=''
)
ax4 = plt.subplot(613, sharex=ax1)
perf.loc[:, 'cash'].plot(
ax=ax4, label='Base Currency ({})'.format(base_currency)
)
ax4.set_ylabel('Cash ({})'.format(base_currency))
perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
ax5 = plt.subplot(614, sharex=ax1)
perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
ax5.set_ylabel('Percent Change')
plt.legend(loc=3)
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
pass
def analyze(context, perf):
# Get the base_currency that was passed as a parameter to the simulation
base_currency = context.exchanges.values()[0].base_currency.upper()
# First chart: Plot portfolio value using base_currency
ax1 = plt.subplot(411)
perf.loc[:, ['portfolio_value']].plot(ax=ax1)
ax1.legend_.remove()
ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
start, end = ax1.get_ylim()
ax1.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
# Second chart: Plot asset price, moving averages and buys/sells
ax2 = plt.subplot(412, sharex=ax1)
perf.loc[:, ['price', 'short_mavg', 'long_mavg']].plot(ax=ax2,
label='Price')
ax2.legend_.remove()
ax2.set_ylabel('{asset}\n({base})'.format(asset=context.asset.symbol,
base=base_currency))
start, end = ax2.get_ylim()
ax2.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
transaction_df = extract_transactions(perf)
if not transaction_df.empty:
buy_df = transaction_df[transaction_df['amount'] > 0]
sell_df = transaction_df[transaction_df['amount'] < 0]
ax2.scatter(buy_df.index.to_pydatetime(),
perf.loc[buy_df.index, 'price'],
marker='^',
s=100,
c='green',
label='')
ax2.scatter(sell_df.index.to_pydatetime(),
perf.loc[sell_df.index, 'price'],
marker='v',
s=100,
c='red',
label='')
# Third chart: Compare percentage change between our portfolio
# and the price of the asset
ax3 = plt.subplot(413, sharex=ax1)
perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
ax3.legend_.remove()
ax3.set_ylabel('Percent Change')
start, end = ax3.get_ylim()
ax3.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
# Fourth chart: Plot our cash
ax4 = plt.subplot(414, sharex=ax1)
perf.cash.plot(ax=ax4)
ax4.set_ylabel('Cash\n({})'.format(base_currency))
start, end = ax4.get_ylim()
ax4.yaxis.set_ticks(np.arange(0, end, end / 5))
plt.show()
def analyze(context=None, perf=None):
end = time.time()
log.info('elapsed time: {}'.format(end - context.start_time))
import matplotlib.pyplot as plt
# The base currency of the algo exchange
base_currency = context.exchanges.values()[0].base_currency.upper()
# Plot the portfolio value over time.
ax1 = plt.subplot(611)
perf.loc[:, 'portfolio_value'].plot(ax=ax1)
ax1.set_ylabel('Portfolio\nValue\n({})'.format(base_currency))
# Plot the price increase or decrease over time.
ax2 = plt.subplot(612, sharex=ax1)
perf.loc[:, 'price'].plot(ax=ax2, label='Price')
ax2.set_ylabel('{asset}\n({base})'.format(asset=context.market.symbol,
base=base_currency))
transaction_df = extract_transactions(perf)
if not transaction_df.empty:
buy_df = transaction_df[transaction_df['amount'] > 0]
sell_df = transaction_df[transaction_df['amount'] < 0]
ax2.scatter(buy_df.index.to_pydatetime(),
perf.loc[buy_df.index.floor('1 min'), 'price'],
marker='^',
s=100,
c='green',
label='')
ax2.scatter(sell_df.index.to_pydatetime(),
perf.loc[sell_df.index.floor('1 min'), 'price'],
marker='v',
s=100,
c='red',
label='')
ax4 = plt.subplot(613, sharex=ax1)
perf.loc[:, 'cash'].plot(ax=ax4,
label='Base Currency ({})'.format(base_currency))
ax4.set_ylabel('Cash\n({})'.format(base_currency))
perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
ax5 = plt.subplot(614, sharex=ax1)
perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
ax5.set_ylabel('Percent\nChange')
ax6 = plt.subplot(615, sharex=ax1)
perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
ax6.set_ylabel('RSI')
ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
if not transaction_df.empty:
ax6.scatter(buy_df.index.to_pydatetime(),
perf.loc[buy_df.index.floor('1 min'), 'rsi'],
marker='^',
s=100,
c='green',
label='')
ax6.scatter(sell_df.index.to_pydatetime(),
perf.loc[sell_df.index.floor('1 min'), 'rsi'],
marker='v',
s=100,
c='red',
label='')
plt.legend(loc=3)
start, end = ax6.get_ylim()
ax6.yaxis.set_ticks(np.arange(0, end, end / 5))
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
pass
