def test_drawdown_details():
drawdown = ffn.to_drawdown_series(df['MSFT'])
drawdown_details = ffn.drawdown_details(drawdown)
assert (drawdown_details.loc[drawdown_details.index[1], 'Length'] == 18)
num_years = 30
num_months = num_years * 12
np.random.seed(0)
returns = np.random.normal(loc=0.06 / 12, scale=0.20 / np.sqrt(12), size=num_months)
returns = pd.Series(np.cumprod(1 + returns))
drawdown = ffn.to_drawdown_series(returns)
drawdown_details = ffn.drawdown_details(drawdown, index_type=drawdown.index)
def test_drawdown_details():
drawdown = ffn.to_drawdown_series(df['MSFT'])
drawdown_details = ffn.drawdown_details(drawdown)
assert (drawdown_details.loc[drawdown_details.index[1], 'Length'] == 18)
num_years = 30
num_months = num_years * 12
np.random.seed(0)
returns = np.random.normal(loc=0.06 / 12, scale=0.20 / np.sqrt(12), size=num_months)
returns = pd.Series(np.cumprod(1 + returns))
drawdown = ffn.to_drawdown_series(returns)
drawdown_details = ffn.drawdown_details(drawdown, index_type=drawdown.index)
def load_and_report():
# fetching the current time as name
name = names_of_fig()
# load Pricing test data
all_contracts, p_sorted = load_data()
# List contracts
print(f"Showing all_contracts..........\n{all_contracts.tail(10)}\n")
print(f"Showing p_sorted......\n{p_sorted.tail(10)}\n")
# output from potential_pair
ret, list_sect = potential_pairs(all_contracts, p_sorted)
print(f"showing list_sect.......\n{list_sect}\n")
print(f"showing ret........\n{ret.tail(10)}\n")
# show the results of in sample testing
ret.iloc[0] = 1
ret.index = all_contracts.index
plt.figure(figsize=(15, 7))
plt.xlabel('Trade Date')
plt.grid(True)
plt.plot(ret)
plt.legend(list(ret.columns))
plt.show()
plt.savefig(os.path.join("charts/sample test", "sample test " + name))
# calculate the performance
perf = ret.calc_stats()
perf.display()
perf.to_csv(sep=',', path="train_perfer.csv")
# plot the maxinum drawndown of each pair
ffn_ret = ffn.to_drawdown_series(ret)
plt.figure(figsize=(15, 7))
plt.grid(True)
plt.plot(ffn_ret)
plt.legend(list(ffn_ret.columns))
plt.show()
plt.savefig(os.path.join("charts/ffn drawdown", "ffn max drawdown " + name))
# In sample back testing of portfolio
port = ret.mean(axis=1)
plt.figure(figsize=(15, 7))
plt.grid(True)
plt.plot(port)
plt.show()
#plt.legend(list(port.columns))
plt.savefig(os.path.join("charts/testing of portfolio", "portfolio test " + name))
perf = port.calc_stats()
print(f"\n\nPrinting perf stats.......\n{perf.stats}\n")
# In sample back testing of portfolio maxinum drawndown
ffn_port = ffn.to_drawdown_series(port)
plt.figure(figsize=(15, 7))
plt.grid(True)
plt.plot(ffn_port)
#plt.legend(list(ffn_port.columns))
plt.savefig(os.path.join("charts/back testing of portfolio maxinum drawndown", "maxinum drawndown " + name))
####################
##sample back testing######
#####################
test_ret, testing_data = sample_backtest(list_sect)
print(f"\n\n\nShowing sample back testing- testing data tail.......\n\n{testing_data.tail(3)}\n")
test_ret.iloc[0] = 1
print(f"\n\nShowing sample backtesting test_ret tail.......\n\n{test_ret.tail(3)}")
print(f"\n\nshowing test_ret shape......\n\n{test_ret.shape})")
print(f"\n\n\nshowing test_ret index........\n\n{test_ret.index}")
# plotting test_ret sample back testing
plt.plot(test_ret)
plt.legend(list(test_ret.columns))
plt.savefig(os.path.join("charts/sample Backtesting/test_ret", "test_ret " + name))
# Out sample back testing of portfolio
port = test_ret.mean(axis=1)
plt.figure(figsize=(15, 7))
plt.grid(True)
plt.plot(port)
# plt.legend(list(port.columns))
plt.savefig(os.path.join("charts/sample Backtesting/portfolio", "backtesting portfolio " + name))
perf = port.calc_stats()
print(perf.stats)
ffn_backtest_sample = ffn.to_drawdown_series(port)
plt.figure(figsize=(15, 7))
plt.grid(True)
plt.plot(ffn_backtest_sample)
# plt.legend(list(ffn_backtest_sample.columns))
plt.savefig(os.path.join("charts/sample Backtesting/ffn_drawdown_port", "drwadown_port " + name))
return None
# Apply Financial Functions for Python (FFN) to calculate statistics of algorithm
equity_curve_df['Equity'] = equity_curve_df
perf = equity_curve_df['Equity'].calc_stats()
# plot equity curve
perf.plot()
plt.show()
# show overall metrics
perf.display()
# display monthly returns
perf.display_monthly_returns()
# plotting visual representation of strategy drawdown series:
ffn.to_drawdown_series(equity_curve_df['Equity']).plot(figsize=(15, 7),
grid=True)
plt.show()
# plot histogram of returns
perf.plot_histogram()
plt.show()
# extract lookback returns
perf.display_lookback_returns()
# *************************************************************************************************************************************************************************************************
# PRINT COLUMN OF SIGNALS FOR THE STRATEGY
print("CURRENT SIGNAL FOR ALGORITHM: 1: BUY, 0: HOLD, -1:SELL")
print(pred2.iloc[:, 2])
# **************************************************************************************************************************************************************************************************
# In[39]:
#equity curve
pairwise_stats.plot()
# In[40]:
#some more metrics
pairwise_stats.display()
# In[41]:
#draw down curve
for pair in pairs:
plt.figure()
ffn.to_drawdown_series(returns_df[pair[0] + " " + pair[1]]).plot(
figsize=(15, 7), grid=True, legend=True)
# In[42]:
pairwise_stats_df = pairwise_stats.stats
# In[43]:
#stores the df of stats
pairwise_stats_df.head()
# In[44]:
def portfolio_return_analysis(df):
# df: returnsdf of pairs