def main():
print("================================")
print(" AIStocksAgents-v1 ")
print("================================")
print("Select source .csv file (/full/path/to/file):")
file = input().strip()
print(
"Select initial date of optimization period (same format as in .csv files):"
)
initial_date = input().strip()
print(
"Select cut date of optimization period (same format as in .csv files):"
)
cut_date = input().strip()
print("Select end date of test period (same format as in .csv files):")
end_date = input().strip()
print("Select number of optimzation cycles:")
ncycles = int(input().strip())
print("Select foder where results will be stored (/full/path/to/folder):")
results = input().strip()
n = 1000 # n: Number of agents
s = 8 # s: Number of strategies per agent
g_a = 60 # g_a: DGA rounds length
g_m = 0.5 # g_m: Mutation rate
g_p = 8 # g_p: Size of the communication pool
p_max = 10 # p_max: Max position held by agent
max_t = 20
df = dat.get_pandas_dataframe(file, 'Open').drop(columns=['index'])
prices = list(df.Price.values)
dates = list(df.Date.values)
Mt = list(df.Mt.values)
Dt = list(df.Dt.values)
Yt = list(df.Yt.values)
initial_idx = dates.index(initial_date)
cut_idx = dates.index(cut_date)
end_idx = dates.index(end_date)
print('Initial date (index in series) is ' + initial_date + '(' +
str(initial_idx) + ')')
print('Cut date (index in series) is ' + cut_date + '(' + str(cut_idx) +
')')
print('End date (index in series) is ' + end_date + '(' + str(end_idx) +
')')
print('Lenght in timesteps of the optimization period: ' +
str(cut_idx - initial_idx))
print('Lenght in timesteps of the test period: ' + str(end_idx - cut_idx))
alg = Alg.Algorithm(prices, Mt, Dt, Yt, n, s, g_a, g_m, g_p, p_max,
ncycles, initial_idx, cut_idx, end_idx, max_t)
start = timer()
print('Starting the simulation...')
pool, all_agents, statistics_opt, avg_wealth_t_test, avg_position_t_test, max_opt, min_opt = alg.run(
)
end = timer() - start
print('Duration of the execution ', f.normalize_seconds(end))
tmp = str(datetime.datetime.now())
directory = results + '/' + tmp
os.mkdir(directory)
last_price_opt = prices[cut_idx]
last_price_test = prices[end_idx]
p.save_histogram(all_agents,
'Number of agents by wealth - Histogram - Optimization',
25, directory + '/histogram-optimization-period.png')
p.save_histogram([a.get_wealth(last_price_test) for a in pool],
'Number of agents by wealth - Histogram - Test', 25,
directory + '/histogram-test-period.png')
# Position + Price chart
plt.rcParams["figure.figsize"] = (12, 8)
fig, ax1 = plt.subplots()
color = 'black'
ax1.set_xlabel('Day')
ax1.set_ylabel('Stcok')
ax1.plot(prices[cut_idx:end_idx], color=color)
ax2 = ax1.twinx()
color = 'orange'
ax2.set_ylabel('Average Position', color=color)
ax2.plot(avg_position_t_test, color=color, linewidth=0.8, alpha=0.7)
ax2.tick_params(axis='y', labelcolor=color)
fig.tight_layout()
plt.savefig(directory + '/position-price-test-chart.png')
# Wealth + Price chart
fig, ax1 = plt.subplots()
color = 'black'
ax1.set_xlabel('Day')
ax1.set_ylabel('Stock')
ax1.plot(prices[cut_idx:end_idx], color=color)
ax2 = ax1.twinx()
color = 'red'
ax2.set_ylabel('Mean Wealth', color=color)
ax2.plot(avg_wealth_t_test, color=color)
ax2.tick_params(axis='y', labelcolor=color)
fig.tight_layout()
plt.savefig(directory + '/wealth-price-test-chart.png')
test_result = [agent.get_wealth(last_price_test) for agent in pool]
file = open(directory + '/report.txt', 'w')
p.out_report(file, alg, statistics_opt, max_opt, min_opt, test_result,
'Stock')
import os
from timeit import default_timer as timer
from src import Plotting as p
import pickle
n = 1000 # n: Numero de agentes
s = 8 # s: Numero de estrategias por agente
g_a = 60 # g_a: periodo de DGA
g_m = 0.5 # g_m: ratio de mutacion
g_p = 8 # g_p: tamao de la 'communication pool'
p_max = 10 # p_max: pos max de cada agente
ncycles = 100 # ncycles: Numero de ciclos en los que el algoritmo recorre el periodo de optimizacion
max_t = 20 # Valor max que pueden alcanzar los valores Tm, Td, Ty dentro de las estragias
stock = 'SP 500'
df = Data.get_pandas_dataframe(r'datasets/SP500.csv',
'Open').drop(columns=['index'])
title = 'SP 500'
prices = list(df.Price.values)
dates = list(df.Date.values)
Mt = list(df.Mt.values)
Dt = list(df.Dt.values)
Yt = list(df.Yt.values)
initial_date = '1990-01-02'
cut_date = '2005-05-12'
end_date = '2012-02-10'
initial_idx = dates.index(initial_date)
cut_idx = dates.index(cut_date)
end_idx = dates.index(end_date)
import os
from timeit import default_timer as timer
from src import Plotting as p
import pickle
n = 1000 # n: Numero de agentes
s = 8 # s: Numero de estrategias por agente
g_a = 60 # g_a: periodo de DGA
g_m = 0.5 # g_m: ratio de mutacion
g_p = 8 # g_p: tamao de la 'communication pool'
p_max = 10 # p_max: pos max de cada agente
ncycles = 100 # ncycles: Numero de ciclos en los que el algoritmo recorre el periodo de optimizacion
max_t = 20 # Max value
stock = 'NKY 225'
df = Data.get_pandas_dataframe(r'datasets/NKY225.csv',
'Close').drop(columns=['index'])
title = 'NKY 225'
prices = list(df.Price.values)
dates = list(df.Date.values)
Mt = list(df.Mt.values)
Dt = list(df.Dt.values)
Yt = list(df.Yt.values)
initial_date = '1993-03-17'
cut_date = '2005-05-12'
end_date = '2012-05-17'
initial_idx = dates.index(initial_date)
cut_idx = dates.index(cut_date)
end_idx = dates.index(end_date)