class PurpleTrader:
#needs to be initialized so as to allow for 62 outputs that return a coordinate
# ES-HyperNEAT specific parameters.
params = {"initial_depth": 3,
"max_depth": 4,
"variance_threshold": 0.00013,
"band_threshold": 0.00013,
"iteration_level": 3,
"division_threshold": 0.00013,
"max_weight": 5.0,
"activation": "tanh"}
# Config for CPPN.
config = neat.config.Config(neat.genome.DefaultGenome, neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet, neat.stagnation.DefaultStagnation,
'config_trader')
start_idx = 0
highest_returns = 0
portfolio_list = []
in_shapes = []
out_shapes = []
def __init__(self, hist_depth):
self.hs = HistWorker()
self.hs.combine_frames()
self.hd = hist_depth
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.currentHists["ZEC"])
self.but_target = .1
self.inputs = self.hs.hist_shaped[0].shape[1]
self.outputs = 1
sign = 1
for ix in range(1,self.inputs+1):
sign = sign *-1
self.in_shapes.append((0.0-(sign*.005*ix), -1.0, 0.0+(sign*.005*ix)))
self.out_shapes.append((0.0, 1.0, 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.epoch_len = 255
#self.node_names = ['x1', 'y1', 'z1', 'x2', 'y2', 'z2', 'weight']
self.leaf_names = []
#num_leafs = 2**(len(self.node_names)-1)//2
for l in range(len(self.in_shapes[0])):
self.leaf_names.append('leaf_one_'+str(l))
self.leaf_names.append('leaf_two_'+str(l))
#self.leaf_names.append('bias')
def set_portfolio_keys(self, folio):
for k in self.hs.currentHists.keys():
folio.ledger[k] = 0
def get_one_epoch_input(self,end_idx):
master_active = []
for x in range(0, self.hd):
active = []
#print(self.outputs)
for y in range(0, self.outputs):
try:
sym_data = self.hs.hist_shaped[y][end_idx-x]
#print(len(sym_data))
active += sym_data.tolist()
except:
print('error')
master_active.append(active)
#print(active)
return master_active
def get_single_symbol_epoch(self, end_idx, symbol_idx):
master_active = []
for x in range(0, self.hd):
try:
sym_data = self.hs.hist_shaped[symbol_idx][end_idx-x]
#print(len(sym_data))
master_active.append(sym_data.tolist())
except:
print('error')
return master_active
def evaluate(self, network, es, rand_start, g, verbose=False):
portfolio_start = .05
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict)
end_prices = {}
buys = 0
sells = 0
if(len(g.connections) > 0.0):
for z in range(rand_start, rand_start+self.epoch_len):
for x in np.random.permutation(self.outputs):
sym = self.hs.coin_dict[x]
active = self.get_single_symbol_epoch(z, x)
network.reset()
for n in range(1, self.hd+1):
out = network.activate(active[self.hd-n])
#print(len(out))
#print(out[x])
#try:
if(out[0] < -.5):
#print("selling")
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
#print("bought ", sym)
elif(out[0] > .5):
#print("buying")
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
#print("sold ", sym)
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hs.currentHists[sym]['close'][self.epoch_len+rand_start]
result_val = portfolio.get_total_btc_value(end_prices)
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
ft = result_val[0]
else:
ft = 0.0
return ft
def solve(self, network):
return self.evaluate(network) >= self.highest_returns
def trial_run(self):
r_start = 0
file = open("es_trade_god_cppn_3d.pkl",'rb')
[cppn] = pickle.load(file)
network = ESNetwork(self.subStrate, cppn, self.params)
net = network.create_phenotype_network_nd()
fitness = self.evaluate(net, network, r_start)
return fitness
def eval_fitness(self, genomes, config):
r_start = randint(0+self.hd, self.hs.hist_full_size - self.epoch_len)
fitter = genomes[0]
fitter_val = 0.0
for idx, g in genomes:
[cppn] = create_cppn(g, config, self.leaf_names, ['cppn_out'])
network = ESNetwork(self.subStrate, cppn, self.params)
net = network.create_phenotype_network_nd()
g.fitness = self.evaluate(net, network, r_start, g)
if(g.fitness > fitter_val):
fitter = g
fitter_val = g.fitness
with open('./champs/perpetual_champion_'+str(fitter.key)+'.pkl', 'wb') as output:
pickle.dump(fitter, output)
print("latest_saved")
class PurpleTrader:
#needs to be initialized so as to allow for 62 outputs that return a coordinate
# ES-HyperNEAT specific parameters.
params = {"initial_depth": 3,
"max_depth": 4,
"variance_threshold": 0.00013,
"band_threshold": 0.00013,
"iteration_level": 3,
"division_threshold": 0.00013,
"max_weight": 5.0,
"activation": "tanh"}
# Config for CPPN.
config = neat.config.Config(neat.genome.DefaultGenome, neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet, neat.stagnation.DefaultStagnation,
'config_trader')
start_idx = 0
highest_returns = 0
portfolio_list = []
in_shapes = []
out_shapes = []
def __init__(self, hist_depth):
self.hs = HistWorker()
self.hs.combine_frames()
self.hd = hist_depth
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.currentHists["ETH"])
self.but_target = .1
self.inputs = self.hs.hist_shaped[0].shape[1]
self.outputs = 1
sign = 1
for ix in range(1,self.inputs+1):
sign = sign *-1
self.in_shapes.append((0.0-(sign*.005*ix), -1.0, 0.0+(sign*.005*ix)))
self.out_shapes.append((0.0, 1.0, 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.epoch_len = 144
#self.node_names = ['x1', 'y1', 'z1', 'x2', 'y2', 'z2', 'weight']
self.leaf_names = []
#num_leafs = 2**(len(self.node_names)-1)//2
for l in range(len(self.in_shapes[0])):
self.leaf_names.append('leaf_one_'+str(l))
self.leaf_names.append('leaf_two_'+str(l))
#self.leaf_names.append('bias')
def set_portfolio_keys(self, folio):
for k in self.hs.currentHists.keys():
folio.ledger[k] = 0
def get_one_epoch_input(self,end_idx):
master_active = []
for x in range(0, self.hd):
active = []
#print(self.outputs)
for y in range(0, self.outputs):
try:
sym_data = self.hs.hist_shaped[y][end_idx-x]
#print(len(sym_data))
active += sym_data.tolist()
except:
print('error')
master_active.append(active)
#print(active)
return master_active
def get_single_symbol_epoch(self, end_idx, symbol_idx):
master_active = []
for x in range(0, self.hd):
try:
sym_data = self.hs.hist_shaped[symbol_idx][end_idx-x]
#print(len(sym_data))
master_active.append(sym_data.tolist())
except:
print('error')
return master_active
def load_net(self, fname):
f = open(fname,'rb')
g = pickle.load(f)
f.close()
[the_cppn] = create_cppn(g, self.config, self.leaf_names, ['cppn_out'])
self.cppn = the_cppn
def run_champs(self):
genomes = os.listdir(os.path.join(os.path.dirname(__file__), 'champs_d2_single'))
fitness_data = {}
best_fitness = 0.0
for g_ix in range(len(genomes)):
genome = self.load_net('./champs_d2_single/'+genomes[g_ix])
start = self.hs.hist_full_size - self.epoch_len
network = ESNetwork(self.subStrate, self.cppn, self.params)
net = network.create_phenotype_network_nd('./champs_visualized2/genome_'+str(g_ix))
fitness = self.evaluate(net, network, start, g_ix, genomes[g_ix])
if fitness > best_fitness:
best_genome = genome
def evaluate(self, network, es, rand_start, g, p_name):
portfolio_start = 1.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict)
end_prices = {}
buys = 0
sells = 0
th = []
with open('./champs_hist2/trade_hist'+p_name + '.txt', 'w') as ft:
ft.write('date,symbol,type,amnt,price,current_balance \n')
for z in range(self.hd, self.hs.hist_full_size -1):
for x in np.random.permutation(self.outputs):
sym = self.hs.coin_dict[x]
active = self.get_single_symbol_epoch(z, x)
network.reset()
for n in range(1, self.hd+1):
out = network.activate(active[self.hd-n])
end_prices[sym] = self.hs.currentHists[sym]['close'][self.hs.hist_full_size-1]
#rng = iter(shuffle(rng))
#print(out[x])
#try:
if(out[0] < -.5):
#print("selling")
did_sell = portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
if did_sell:
ft.write(str(self.hs.currentHists[sym]['date'][z]) + ",")
ft.write(sym +",")
ft.write('sell,')
ft.write(str(portfolio.ledger[sym])+",")
ft.write(str(self.hs.currentHists[sym]['close'][z])+",")
ft.write(str(portfolio.get_total_btc_value_no_sell(end_prices)[0])+ " \n")
#print("bought ", sym)
elif(out[0] > .5):
did_buy = portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
if did_buy:
ft.write(str(self.hs.currentHists[sym]['date'][z]) + ",")
ft.write(sym +",")
ft.write('buy,')
ft.write(str(portfolio.target_amount)+",")
ft.write(str(self.hs.currentHists[sym]['close'][z])+",")
ft.write(str(portfolio.get_total_btc_value_no_sell(end_prices)[0])+ " \n")
#print("sold ", sym)
#skip the hold case because we just dont buy or sell heh
result_val = portfolio.get_total_btc_value(end_prices)
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2], p_name)
ft = result_val[0]
return ft
def solve(self, network):
return self.evaluate(network) >= self.highest_returns
def report_back(self, portfolio, prices):
print(portfolio.get_total_btc_value(prices))
def trial_run(self):
r_start = 0
file = open("es_trade_god_cppn_3d.pkl",'rb')
[cppn] = pickle.load(file)
network = ESNetwork(self.subStrate, cppn, self.params)
net = network.create_phenotype_network_nd()
fitness = self.evaluate(net, network, r_start)
return fitness
def eval_fitness(self, genomes, config):
r_start = randint(0+self.hd, self.hs.hist_full_size - self.epoch_len)
fitter = genomes[0]
fitter_val = 0.0
for idx, g in genomes:
[cppn] = create_cppn(g, config, self.leaf_names, ['cppn_out'])
network = ESNetwork(self.subStrate, cppn, self.params)
net = network.create_phenotype_network_nd('current_net.png')
g.fitness = self.evaluate(net, network, r_start)
if(g.fitness > fitter_val):
fitter = g
fitter_val = g.fitness
with open('./champs/perpetual_champion_'+str(fitter.key)+'.pkl', 'wb') as output:
pickle.dump(fitter, output)
print("latest_saved")