def rebalance_porfolio(date, asset, companies_data):
# date_string = datetime.datetime.strftime(date, "%Y-%m-%d")
print("=" * 50)
print("Rebalancing Portfolio For Current Date: ", date)
print("=" * 50)
# Load Trained Model
lstm = LstmModel(name="Test2", load=True)
# Save Values In Dict
companies_rtv = dict()
companies_prices = dict()
# Predict One-Week Price
# Calculate Return-To-Volatility
# Save The Result For Different Companies
day_in, day_want, step = 50, 10, 2
for company, data in companies_data.items():
# Predictions From LSTM
idx = data[data['Date'] == date].index.item()
_input = data.iloc[idx - day_in:idx + day_want - step + 1,
1:data.shape[1]]
predictions = pd.DataFrame(lstm.predict(_input),
index=pd.date_range(start=date, periods=10))
# Calculate Return-To-Volatility
rtv = calculate_rtv(predictions)
companies_rtv[company] = rtv
# Get Price List
companies_prices[company] = data.iloc[idx, 5]
# Sort The Results Descending & Choose Top 10 Companies
filtered_list = list(filter(lambda x: x[1] >= 0, companies_rtv.items()))
sorted_list = sorted(filtered_list, key=lambda x: x[1], reverse=True)
companies_rtv_filter = {t[0]: t[1] for t in sorted_list[:10]}
# Build Portfolio
rtv_sum = sum(companies_rtv_filter.values())
portfolio_pct = {
key: (value / rtv_sum)
for key, value in companies_rtv_filter.items()
}
portfolio = {
company: ((asset * pct) / companies_prices[company])
for company, pct in portfolio_pct.items()
}
print("Company Prices: ", companies_prices)
print("Companies Return-To-Volatility: ", companies_rtv)
print("Filtered 10 Companies: ", companies_rtv_filter)
print("Portfolio Percentage: ", portfolio_pct)
print("Portfolio: ", portfolio)
return portfolio, portfolio_pct
def dummy_porfolio(date, asset, companies_data):
lstm = LstmModel(name="Test2", load=True)
day_in, day_want, step = 50, 10, 2
for company, data in companies_data.items():
# Predictions From LSTM
idx = data[data['Date'] == date].index.item()
print(data.iloc[idx - day_in:idx + day_want - step + 1,
0:data.shape[1]])
_input = data.iloc[idx - day_in:idx + day_want - step + 1,
1:data.shape[1]]
predictions = pd.DataFrame(lstm.predict(_input),
index=pd.date_range(start=date, periods=10))
break
return {}, {}
def rebalance_porfolio(date, asset, companies_data):
# date_string = datetime.datetime.strftime(date, "%Y-%m-%d")
print("=" * 50)
print("Rebalancing Portfolio For Current Date: ", date)
print("=" * 50)
# Load Trained Model
lstm = LstmModel(name="Old1", load=True)
# Save Values In Dict
companies_rtv = dict()
companies_prices = dict()
# Predict One-Week Price
# Calculate Return-To-Volatility
# Save The Result For Different Companies
day_in, day_out, num_features = lstm.load_settings()
for company, data in companies_data.items():
# Current Date's Index
try:
idx = data[data['Date'] == date].index.values[0]
except IndexError: # When Company Data Is Empty On That Date
continue
# Predictions From LSTM
_input = data.iloc[idx - day_in:idx]['Adj Close'].values
_input = np.reshape(_input, (_input.shape[0], 1))
predictions = pd.DataFrame(lstm.predict(company=company,
_input=_input),
index=pd.date_range(start=date,
periods=day_out))
# Calculate Return-To-Volatility
rtv = calculate_rtv(predictions)
companies_rtv[company] = rtv
# Get Adj Closing Price List
companies_prices[company] = data.iloc[idx, 4]
# Sort The Results Descending & Choose Top 10 Companies
filtered_list = list(filter(lambda x: x[1] > 0, companies_rtv.items()))
sorted_list = sorted(filtered_list, key=lambda x: x[1], reverse=True)
companies_rtv_filter = {t[0]: t[1] for t in sorted_list[:10]}
# Build Portfolio
rtv_sum = sum(companies_rtv_filter.values())
portfolio_pct = {
key: (value / rtv_sum)
for key, value in companies_rtv_filter.items()
}
portfolio = {
company: ((asset * pct) / companies_prices[company])
for company, pct in portfolio_pct.items()
}
print("Company Prices: ", companies_prices)
print("Companies Return-To-Volatility: ", companies_rtv)
print("Filtered 10 Companies: ", companies_rtv_filter)
print("Portfolio Percentage: ", portfolio_pct)
print("Portfolio: ", portfolio)
return portfolio, portfolio_pct