def DoWork(source_file, m, k, fltr, trade_cost, testing_start_date,
testing_end_date):
# Set some defaults
p = Predictor(source_file, m, k, fltr, testing_start_date,
testing_end_date)
p.read_file_to_daily_data_by_weeks(0)
p.calc_historical_weekly_return(p.daily_data_by_weeks)
# Initialize variables
strategy_trade_count = 0
in_market_count = 0
current_state = 0
strategy_capital = 100
bh_capital = 100
strategy_weekly_return = []
bh_weekly_return = []
for idx in range(p.start_split, p.end_split):
# Generate weekly signals
p.weekly_return_data = p.historical_weekly_return_data[:idx - 1]
alist = p.find_k_closest_histories()
r = p.calc_next_week_return(alist)
cur_signal = p.signal(current_state, r)
# Signal handling
cur_index_price = p.daily_data_by_weeks[idx][-1][2]
prev_index_price = p.daily_data_by_weeks[idx - 1][-1][2]
actual_return = math.log(cur_index_price) - math.log(prev_index_price)
if current_state != cur_signal:
current_state = cur_signal
strategy_trade_count += 1
strategy_capital = strategy_capital * (1 - trade_cost)
# K Nearest Neighbor
strategy_capital = strategy_capital * (1 +
actual_return * current_state)
strategy_weekly_return.append(actual_return)
# Buy-and-Hold
if current_state == 1:
#in_market_count += 1
bh_capital = bh_capital * (1 + actual_return)
bh_weekly_return.append(actual_return)
print('{0}, {1}, {2}'.format(p.daily_data_by_weeks[idx - 1][-1][0],
strategy_capital, bh_capital))
def DoWork(source_file, m, k, fltr, trade_cost, testing_start_date, testing_end_date):
# Set some defaults
p = Predictor(source_file, m, k, fltr, testing_start_date, testing_end_date)
p.read_file_to_daily_data_by_weeks(0)
p.calc_historical_weekly_return(p.daily_data_by_weeks)
# Initialize variables
strategy_trade_count = 0
in_market_count = 0
current_state = 0
strategy_capital = 100
bh_capital = 100
strategy_weekly_return = []
bh_weekly_return = []
for idx in range(p.start_split, p.end_split):
# Generate weekly signals
p.weekly_return_data = p.historical_weekly_return_data[:idx-1]
alist = p.find_k_closest_histories()
r = p.calc_next_week_return(alist)
cur_signal = p.signal(current_state, r)
# Signal handling
cur_index_price = p.daily_data_by_weeks[idx][-1][2]
prev_index_price = p.daily_data_by_weeks[idx-1][-1][2]
actual_return = math.log(cur_index_price) - math.log(prev_index_price)
if current_state != cur_signal:
current_state = cur_signal
strategy_trade_count += 1
strategy_capital = strategy_capital * (1 - trade_cost)
# K Nearest Neighbor
strategy_capital = strategy_capital * (1 + actual_return * current_state)
strategy_weekly_return.append(actual_return)
# Buy-and-Hold
if current_state == 1:
#in_market_count += 1
bh_capital = bh_capital * (1 + actual_return)
bh_weekly_return.append(actual_return)
print('{0}, {1}, {2}'.format(p.daily_data_by_weeks[idx-1][-1][0], strategy_capital, bh_capital))
def DoWork(source_file, m, k, fltr, trade_cost, testing_start_date, testing_end_date):
# Set some defaults
p = Predictor(source_file, m, k, fltr, testing_start_date, testing_end_date)
p.read_file_to_daily_data_by_weeks(0)
p.calc_historical_weekly_return(p.daily_data_by_weeks)
# Initialize variables
strategy_trade_count = 0
in_market_count = 0
current_state = 0
long_position_r = 0
short_position_r = 0
bh_return = 0
strategy_weekly_return = []
bh_weekly_return = []
for idx in range(p.start_split, p.end_split):
# Generate weekly signals
p.weekly_return_data = p.historical_weekly_return_data[:idx-1]
alist = p.find_k_closest_histories()
r = p.calc_next_week_return(alist)
cur_signal = p.signal(current_state, r)
# Signal handling
cur_index_price = p.daily_data_by_weeks[idx][-1][2]
prev_index_price = p.daily_data_by_weeks[idx-1][-1][2]
actual_return = math.log(cur_index_price) - math.log(prev_index_price)
if current_state != cur_signal:
current_state = cur_signal
strategy_trade_count += 1
# K Nearest Neighbor
if current_state == 1:
long_position_r += actual_return
strategy_weekly_return.append(actual_return)
elif current_state == -1:
short_position_r += actual_return * current_state
strategy_weekly_return.append(actual_return * current_state)
# Buy-and-Hold
if current_state == 1:
in_market_count += 1
bh_return += actual_return
bh_weekly_return.append(actual_return)
'''
if current_state == 0:
decision = 'No position!'
elif actual_return * current_state > 0:
decision = 'Prediction is RIGHT!'
else:
decision = 'Prediction is WRONG!'
print('Predicted return = {0} and actual return = {1} - {2}'.format(r, actual_return, decision))
'''
# Excess return
trade_cost_coef = math.log((1-trade_cost)/(1+trade_cost))
strategy_return = long_position_r + short_position_r + strategy_trade_count * trade_cost_coef
bh_return = (in_market_count/float(p.end_split - p.start_split)) * bh_return + 2 * trade_cost_coef
print('Strategy return = {0} (long: {1} and short: {2}), bh return = {3}, excess return = {4}, sharpe = {5}'.format(strategy_return, long_position_r, short_position_r, bh_return, strategy_return-bh_return, strategy_return/Stdev(strategy_weekly_return)))
def DoWork(source_file, m, k, fltr, trade_cost, testing_start_date,
testing_end_date):
# Set some defaults
p = Predictor(source_file, m, k, fltr, testing_start_date,
testing_end_date)
p.read_file_to_daily_data_by_weeks(0)
p.calc_historical_weekly_return(p.daily_data_by_weeks)
# Initialize variables
strategy_trade_count = 0
in_market_count = 0
current_state = 0
long_position_r = 0
short_position_r = 0
bh_return = 0
strategy_weekly_return = []
bh_weekly_return = []
for idx in range(p.start_split, p.end_split):
# Generate weekly signals
p.weekly_return_data = p.historical_weekly_return_data[:idx - 1]
alist = p.find_k_closest_histories()
r = p.calc_next_week_return(alist)
cur_signal = p.signal(current_state, r)
# Signal handling
cur_index_price = p.daily_data_by_weeks[idx][-1][2]
prev_index_price = p.daily_data_by_weeks[idx - 1][-1][2]
actual_return = math.log(cur_index_price) - math.log(prev_index_price)
if current_state != cur_signal:
current_state = cur_signal
strategy_trade_count += 1
# K Nearest Neighbor
if current_state == 1:
long_position_r += actual_return
strategy_weekly_return.append(actual_return)
elif current_state == -1:
short_position_r += actual_return * current_state
strategy_weekly_return.append(actual_return * current_state)
# Buy-and-Hold
if current_state == 1:
in_market_count += 1
bh_return += actual_return
bh_weekly_return.append(actual_return)
'''
if current_state == 0:
decision = 'No position!'
elif actual_return * current_state > 0:
decision = 'Prediction is RIGHT!'
else:
decision = 'Prediction is WRONG!'
print('Predicted return = {0} and actual return = {1} - {2}'.format(r, actual_return, decision))
'''
# Excess return
trade_cost_coef = math.log((1 - trade_cost) / (1 + trade_cost))
strategy_return = long_position_r + short_position_r + strategy_trade_count * trade_cost_coef
bh_return = (in_market_count / float(p.end_split - p.start_split)
) * bh_return + 2 * trade_cost_coef
print(
'Strategy return = {0} (long: {1} and short: {2}), bh return = {3}, excess return = {4}, sharpe = {5}'
.format(strategy_return, long_position_r, short_position_r, bh_return,
strategy_return - bh_return,
strategy_return / Stdev(strategy_weekly_return)))
