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 evaluate(self, network, verbose=False):
portfolio = CryptoFolio(1, self.hs.coin_dict)
active = []
results = {}
end_prices = {}
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
rand_start = randint(0, self.hs.hist_full_size - 89) #get random start point with a week of padding from end
for z in range(rand_start, rand_start+89):
'''
if(z == 0):
old_idx = 1
else:
old_idx = z * 5
new_idx = (z + 1) * 5
'''
active = self.get_one_bar_input_2d(z)
results[z] = network.feed(active)
for i in range(rand_start, rand_start+89):
out = results[i]
for x in range(0, self.outputs):
sym = self.hs.coin_dict[x]
if(out[x] == 1.0):
portfolio.buy_coin(sym, self.hs.currentHists[sym][x]['close'])
elif(out[x] == 0.0):
portfolio.sell_coin(sym, self.hs.currentHists[sym][x]['close'])
for y in range(len(out)):
end_prices[self.hs.coin_dict[y]] = self.hs.hist_shaped[y][89][2]
result_val = portfolio.get_total_btc_value(end_prices)
print(results)
if(results > self.highest_returns):
self.highest_returns = results
return results
def evaluate_champ(self, network, es, rand_start, g, verbose=False):
portfolio_start = 10000.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
buys = 0
sells = 0
with open("./trade_hists/" + str(g.key) + "_hist.txt", "w") as ft:
ft.write('date,current_balance \n')
for z_minus in range(0, self.epoch_len - 1):
#TODO add comments to clarify all the
#shit im doing here
z = rand_start - z_minus
active = self.get_one_epoch_input(z)
buy_signals = []
buy_syms = []
sell_syms = []
sell_signals = []
network.reset()
#activate net fully
for n in range(1, self.hd+1):
out = network.activate(active[self.hd-n])
for x in range(len(out)):
sym = self.hs.coin_dict[x]
end_prices[sym] = self.hs.currentHists[sym]['close_price'][z]
if(out[x] > .5):
buy_signals.append(out[x])
buy_syms.append(sym)
if(out[x] < -.5):
sell_signals.append(out[x])
sell_syms.append(sym)
sorted_buys = np.argsort(buy_signals)[::-1]
sorted_sells = np.argsort(sell_signals)
#print(len(sorted_shit), len(key_list))
for x in sorted_sells:
sym = sell_syms[x]
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close_price'][z])
for x in sorted_buys:
sym = buy_syms[x]
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close_price'][z])
for x in range(len(out)):
sym = self.hs.coin_dict[x]
if (out[x] > .5):
portfolio.buy_coin(sym, self.hs.currentHists[sym]['open_price'][z])
if (out[x] < -.5):
portfolio.sell_coin(sym, self.hs.currentHists[sym]['open_price'][z])
end_prices[sym] = self.hs.currentHists[sym]['open_price'][z]
ft.write(self.hs.currentHists[sym]['begins_at'][z] + ",")
ft.write(str(portfolio.get_total_btc_value_no_sell(end_prices)[0])+ " \n")
result_val = portfolio.get_total_btc_value(end_prices)
print("genome id ", g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
if result_val[1] == 0:
ft = .7
else:
ft = result_val[0]
return ft
def evaluate(self, builder, rand_start, g, verbose=False):
portfolio_start = 1.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
phenotypes = {}
buys = 0
sells = 0
last_val = portfolio_start
ft = 0.0
for z_minus in range(0, self.epoch_len):
for x in range(self.num_syms):
#TODO add comments to clarify all the
#shit im doing here
sym = self.hs.coin_dict[x]
z = rand_start - z_minus
pos_size = portfolio.ledger[sym]
active = self.get_single_symbol_epoch_recurrent_with_position_size(z, x, pos_size)
#print(active)
if(z_minus == 0 or (z_minus + 1) % 8 == 0):
self.reset_substrate(active[0])
builder.substrate = self.substrate
phenotypes[sym] = builder.create_phenotype_network_nd()
network = phenotypes[sym]
network.reset()
for n in range(1, self.hd+1):
network.activate([active[self.hd-n]])
out = network.activate([active[0]])
#print(out[0])
if(out[0] < 0.5):
#print("selling")
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
#print("bought ", sym)
elif(out[0] > 0.5):
#print("buying")
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
#rng = iter(shuffle(rng))
end_prices[sym] = self.hs.currentHists[sym]['close'][z]
bal_now = portfolio.get_total_btc_value_no_sell(end_prices)[0]
ft += bal_now - last_val
last_val = bal_now
result_val = portfolio.get_total_btc_value(end_prices)
print(g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
'''
if result_val[0] == portfolio_start:
ft = -.2
'''
return ft
def evaluate(self, network, es, rand_start, g, verbose=False):
portfolio_start = 1.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
buys = 0
sells = 0
last_val = portfolio_start
ft = 0.0
for z_minus in range(0, self.epoch_len):
#TODO add comments to clarify all the
#shit im doing here
z = rand_start - z_minus
active = self.get_one_epoch_input(z)
buy_signals = []
buy_syms = []
sell_syms = []
sell_signals = []
network.reset()
for n in range(1, self.hd + 1):
network.activate(active[self.hd - n])
out = network.activate(active[0])
for x in range(len(out)):
sym = self.hs.coin_dict[x]
end_prices[sym] = self.hs.currentHists[sym]['close'][z]
if (out[x] > .5):
buy_signals.append(out[x])
buy_syms.append(sym)
if (out[x] < -.5):
sell_signals.append(out[x])
sell_syms.append(sym)
#rng = iter(shuffle(rng))
sorted_buys = np.argsort(buy_signals)[::-1]
sorted_sells = np.argsort(sell_signals)
#print(len(sorted_shit), len(key_list))
for x in sorted_sells:
sym = sell_syms[x]
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
for x in sorted_buys:
sym = buy_syms[x]
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
bal_now = portfolio.get_total_btc_value_no_sell(end_prices)[0]
ft += bal_now - last_val
last_val = bal_now
result_val = portfolio.get_total_btc_value(end_prices)
print(g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
if result_val[0] == portfolio_start:
ft = ft * .9
return ft
def evaluate(self, network, es, rand_start, g, verbose=False):
portfolio_start = 1.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
buys = 0
sells = 0
for z in range(rand_start, rand_start + self.epoch_len):
#TODO add comments to clarify all the
#shit im doing here
active = self.get_one_epoch_input(z)
buy_signals = []
buy_syms = []
sell_syms = []
sell_signals = []
network.reset()
for n in range(1, self.hd + 1):
network.activate(active[self.hd - n])
out = network.activate(active[0])
for x in range(len(out)):
if (z > (self.epoch_len + rand_start) - 2):
sym = self.hs.coin_dict[x]
end_prices[sym] = self.hs.currentHists[sym]['close'][
self.epoch_len + rand_start]
if (out[x] > .5):
buy_signals.append(out[x])
buy_syms.append(self.hs.coin_dict[x])
if (out[x] < -.5):
sell_signals.append(out[x])
sell_syms.append(self.hs.coin_dict[x])
#rng = iter(shuffle(rng))
sorted_buys = np.argsort(buy_signals)[::-1]
sorted_sells = np.argsort(sell_signals)
#print(len(sorted_shit), len(key_list))
for x in sorted_sells:
sym = sell_syms[x]
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
for x in sorted_buys:
sym = buy_syms[x]
portfolio.target_amount = .1 + (out[x] * .1)
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
result_val = portfolio.get_total_btc_value(end_prices)
print(g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
if result_val[1] == 0:
ft = .7
else:
ft = result_val[0]
return ft
def evaluate(self, g, config):
rand_start = self.rand_start
[cppn] = create_cppn(g, config, self.leaf_names, ['cppn_out'])
net = ESNetwork(self.subStrate, cppn, self.params)
network = net.create_phenotype_network_nd()
portfolio_start = 1.0
key_list = list(self.hs.currentHists.keys())
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):
active = self.get_one_epoch_input(z)
signals = []
network.reset()
for n in range(1, self.hd + 1):
out = network.activate(active[self.hd - n])
for x in range(len(out)):
signals.append(out[x])
#rng = iter(shuffle(rng))
sorted_shit = np.argsort(signals)[::-1]
for x in sorted_shit:
sym = self.hs.coin_dict[x]
#print(out[x])
#try:
if (out[x] < -.5):
#print("selling")
portfolio.sell_coin(
sym, self.hs.currentHists[sym]['close'][z])
#print("bought ", sym)
if (out[x] > .5):
#print("buying")
portfolio.target_amount = .1 + (out[x] * .1)
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
if (z > self.epoch_len + rand_start - 2):
end_prices[sym] = self.hs.currentHists[sym]['close'][z]
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 evaluate(self, builder, rand_start, g, verbose=False):
portfolio_start = 1000.0
end_prices = {}
phenotypes = {}
balances = []
buys = 0
sells = 0
last_val = portfolio_start
ft = 0.0
for x in range(self.num_syms):
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USD")
portfolio.target_amount = .25
sym = self.hs.coin_dict[x]
for z_minus in range(0, self.epoch_len):
z = rand_start - z_minus
pos_size = portfolio.ledger[sym]
active = self.get_single_symbol_epoch_recurrent_with_position_size(z, x, pos_size)
#print(active)
if(z_minus == 0 or (z_minus + 1) % 8 == 0):
self.reset_substrate(active[0])
builder.substrate = self.substrate
phenotypes[sym] = builder.create_phenotype_network_nd()
network = phenotypes[sym]
network.reset()
for n in range(1, self.hd+1):
network.activate([active[self.hd-n]])
out = network.activate([active[0]])
if(out[0] < 0.0):
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
#print("bought ", sym)
elif(out[0] > 0.0):
did_buy = portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
#rng = iter(shuffle(rng))
end_prices[sym] = self.hs.currentHists[sym]['close'][z]
bal_now = portfolio.get_total_btc_value_no_sell(end_prices)[0]
if bal_now == last_val:
ft += -.01
else:
ft += bal_now - last_val
last_val = bal_now
print(g.key, " : ")
print(ft)
#print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
return ft
def evaluate(self, network, es, rand_start, g, verbose=False):
portfolio_start = 10000.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
buys = 0
sells = 0
loss_factor = 0
ft = 0
last_val = portfolio_start
for z_minus in range(0, self.epoch_len - 1):
#TODO add comments to clarify all the
#shit im doing here
z = rand_start - z_minus
active = self.get_one_epoch_input(z)
buy_signals = []
buy_syms = []
sell_syms = []
sell_signals = []
network.reset()
for n in range(1, self.hd+1):
out = network.activate(active[self.hd-n])
if (out[0] > .5):
#print("buying " + sym)
portfolio.buy_coin("SPXL", self.hs.currentHists["SPXL"]['close_price'][z])
if (out[0] < -.5):
#print("selling " + sym)
portfolio.buy_coin("SPXS", self.hs.currentHists["SPXS"]['close_price'][z])
if (out[0] > -.5 and out[0] < .5):
portfolio.sell_coin("SPXL", self.hs.currentHists["SPXL"]['close_price'][z])
portfolio.sell_coin("SPXS", self.hs.currentHists["SPXS"]['close_price'][z])
end_prices["SPXS"] = self.hs.currentHists["SPXS"]['close_price'][z]
end_prices["SPXL"] = self.hs.currentHists["SPXL"]['close_price'][z]
bal_now = portfolio.get_total_btc_value_no_sell(end_prices)[0]
ft += bal_now - last_val
last_val = bal_now
result_val = portfolio.get_total_btc_value(end_prices)
print("genome id ", g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
'''
if result_val[1] == 0:
ft = .5
else:
ft = result_val[0] - loss_factor
'''
return ft
def evaluate_champ(self, builder, rand_start, g, verbose=False):
portfolio_start = 1000.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
phenotypes = {}
buys = 0
sells = 0
with open("./trade_hists/kraken/" + str(g.key) + "_hist.txt",
"w") as ft:
ft.write('date,current_balance \n')
for z_minus in range(0, self.epoch_len - 1):
for x in range(self.num_syms):
z = rand_start - z_minus
sym = self.hs.coin_dict[x]
z = rand_start - z_minus
pos_size = portfolio.ledger[sym]
active = self.get_single_symbol_epoch_recurrent_with_position_size(
z, x, pos_size)
if (z_minus == 0 or (z_minus + 1) % 8 == 0):
self.reset_substrate(active[0])
builder.substrate = self.substrate
phenotypes[sym] = builder.create_phenotype_network_nd()
network = phenotypes[sym]
network.reset()
for n in range(1, self.hd + 1):
network.activate([active[self.hd - n]])
out = network.activate([active[0]])
end_prices[sym] = self.hs.currentHists[sym]['close'][z]
if (out[0] < .5 or (z_minus + 1) % 16 == 0):
portfolio.sell_coin(
sym, self.hs.currentHists[sym]['close'][z])
else:
portfolio.buy_coin(
sym, self.hs.currentHists[sym]['close'][z])
ft.write(str(self.hs.currentHists[sym]['time'][z]) + ",")
ft.write(
str(
portfolio.get_total_btc_value_no_sell(end_prices)
[0]) + " \n")
result_val = portfolio.get_total_btc_value(end_prices)
print("genome id ", g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ",
result_val[2])
ft = result_val[0]
return ft
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):
active = self.get_one_epoch_input(z)
network.reset()
for n in range(1, self.hd + 1):
out = network.activate(active[self.hd - n])
for x in range(len(out)):
signals.append(out[x])
#rng = iter(shuffle(rng))
sorted_shit = np.argsort(signals)[::-1]
#print(sorted_shit, len(sorted_shit))
#print(len(sorted_shit), len(key_list))
for x in sorted_shit:
sym = self.hs.coin_dict[x]
#print(out[x])
#try:
if (out[x] < -.5):
#print("selling")
portfolio.sell_coin(
sym, self.hs.currentHists[sym]['close'][z])
#print("bought ", sym)
if (out[x] > .5):
#print("buying")
portfolio.target_amount = .1 + (out[x] * .1)
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
if (z == self.epoch_len + rand_start):
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 evaluate_champ(self, network, es, rand_start, g, verbose=False):
portfolio_start = 10000.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
buys = 0
sells = 0
with open("./trade_hists/" + str(g.key) + "_hist.txt", "w") as ft:
ft.write('date,current_balance \n')
for z in range(rand_start, rand_start + self.epoch_len):
#TODO add comments to clarify all the
#shit im doing here
active = self.get_net_input(z)
buy_signals = []
buy_syms = []
sell_syms = []
sell_signals = []
network.reset()
for n in range(1, self.hd + 1):
network.activate(active[self.hd - n])
out = network.activate(active[0])
for x in range(len(out)):
sym = self.hs.coin_dict[x]
if (out[x] > .5):
portfolio.buy_coin(
sym, self.hs.currentHists[sym]['open_price'][z])
if (out[x] < -.5):
portfolio.sell_coin(
sym, self.hs.currentHists[sym]['open_price'][z])
end_prices[sym] = self.hs.currentHists[sym]['open_price'][
self.epoch_len + rand_start]
ft.write(self.hs.currentHists[sym]['begins_at'][z] + ",")
ft.write(
str(portfolio.get_total_btc_value_no_sell(end_prices)[0]) +
" \n")
result_val = portfolio.get_total_btc_value(end_prices)
print("genome id ", g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ",
result_val[2])
if result_val[1] == 0:
ft = .7
else:
ft = result_val[0]
return ft
def evaluate_champ(self, builder, rand_start, g, champ_num, verbose=False):
end_prices = {}
phenotypes = {}
buys = 0
sells = 0
pathlib.Path(str(pathlib.Path(__file__).parent.absolute()) + '/trade_hists/binance_per_symbol/champ_' + str(champ_num)).mkdir(exist_ok=True)
balances = []
for x in range(self.num_syms):
sym = self.hs.coin_dict[x]
portfolio = CryptoFolio(1000.0, self.hs.coin_dict, "USD")
with open("./trade_hists/binance_per_symbol/champ_" + str(champ_num) + "/" + sym + "_hist.txt", "w") as ft:
ft.write('0,1\n')
for z_minus in range(0, self.epoch_len - 1):
z = rand_start - z_minus
pos_size = portfolio.ledger[sym]
active = self.get_single_symbol_epoch_recurrent_with_position_size(z, x, pos_size)
if(z_minus == 0 or (z_minus + 1) % 8 == 0):
self.reset_substrate(active[0])
builder.substrate = self.substrate
phenotypes[sym] = builder.create_phenotype_network_nd()
network = phenotypes[sym]
network.reset()
for n in range(1, self.hd+1):
network.activate([active[self.hd-n]])
out = network.activate([active[0]])
end_prices[sym] = self.hs.currentHists[sym]['close'][z]
if(out[0] < 0.0):
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
if(out[0] > 0.0):
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
balance = portfolio.get_total_btc_value_no_sell(end_prices)[0]
ft.write(str(self.hs.currentHists[sym]['date'][z]) + ",")
ft.write(str(balance)+ " \n")
result_val = portfolio.get_total_btc_value(end_prices)
balances.append(result_val[0])
print("genome id ", g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
return np.asarray(balances, dtype=np.float32).mean()
def evaluate(self, network, es, rand_start, verbose=False):
portfolio = CryptoFolio(.05, self.hs.coin_dict)
end_prices = {}
buys = 0
sells = 0
for z in range(rand_start, rand_start+self.epoch_len):
'''
if(z == 0):
old_idx = 1
else:
old_idx = z * 5
new_idx = (z + 1) * 5
'''
active = self.get_one_bar_input_2d(z)
network.reset()
for n in range(es.activations):
out = network.activate(active)
#print(len(out))
rng = len(out)
#rng = iter(shuffle(rng))
for x in np.random.permutation(rng):
sym = self.hs.coin_dict[x]
#print(out[x])
try:
if(out[x] < -.5):
#print("selling")
portfolio.sell_coin(sym, self.hs.currentHists[sym]['close'][z])
elif(out[x] > .5):
#print("buying")
portfolio.buy_coin(sym, self.hs.currentHists[sym]['close'][z])
except:
print('error', sym)
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hs.hist_shaped[x][self.epoch_len][2]
result_val = portfolio.get_total_btc_value(end_prices)
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
return result_val[0]
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
class PaperTrader:
params = {
"initial_depth": 3,
"max_depth": 4,
"variance_threshold": 0.00013,
"band_threshold": 0.00013,
"iteration_level": 3,
"division_threshold": 0.00013,
"max_weight": 8.0,
"activation": "tanh"
}
# Config for CPPN.
config = neat.config.Config(neat.genome.DefaultGenome,
neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet,
neat.stagnation.DefaultStagnation,
'config_trader')
in_shapes = []
out_shapes = []
def __init__(self, ticker_len, start_amount, histdepth, base_sym):
self.trade_hist = {}
self.polo = Poloniex()
self.hd = histdepth
self.ticker_len = ticker_len
self.end_ts = datetime.now() + timedelta(seconds=(ticker_len * 24))
self.start_amount = start_amount
self.hs = HistWorker()
self.hs.combine_live_usd_frames()
print(self.hs.currentHists.keys())
self.end_idx = len(self.hs.hist_shaped[0]) - 1
self.but_target = .1
self.inputs = self.hs.hist_shaped.shape[0] * (
self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
self.folio = CryptoFolio(start_amount,
list(self.hs.currentHists.keys()), base_sym)
self.base_sym = base_sym
sign = 1
for ix in range(1, self.outputs + 1):
sign = sign * -1
self.out_shapes.append((0.0 - (sign * .005 * ix), 0.0, -1.0))
for ix2 in range(1, (self.inputs // self.outputs) + 1):
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 0.0))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
self.load_net()
print(self.hs.coin_dict)
self.poloTrader()
def refresh_data(self):
try:
self.hs.pull_polo_usd_live(21)
self.hs.combine_live_usd_frames()
self.end_idx = len(self.hs.hist_shaped[0]) - 1
except Exception as e:
print(e)
time.sleep(360)
self.refresh_data()
return
def load_net(self):
champ_file = open("./champ_data/latest_greatest.pkl", 'rb')
g = pickle.load(champ_file)
#file.close()
the_cppn = neat.nn.FeedForwardNetwork.create(g, self.config)
self.cppn = the_cppn
def make_shapes(self):
self.in_shapes = []
self.out_shapes = []
sign = 1
for ix in range(1, self.outputs + 1):
sign = sign * -1
self.out_shapes.append((0.0 - (sign * .005 * ix), -1.0, -1.0))
for ix2 in range(1, (self.inputs // self.outputs) + 1):
self.in_shapes.append(
(0.0 + (sign * .01 * ix2), 0.0 - (sign * .01 * ix2), 1.0))
def reset_tickers(self):
try:
self.tickers = self.polo.returnTicker()
except Exception as e:
time.sleep(360)
self.reset_tickers()
return
def get_price(self, coin):
return self.tickers[coin]['last']
def get_current_balance(self):
#self.refresh_data()
self.reset_tickers()
c_prices = {}
for s in self.hs.currentHists.keys():
if s != self.base_sym:
c_prices[s] = self.get_price(self.base_sym + "_" + s)
return self.folio.get_total_btc_value_no_sell(c_prices)
def get_one_bar_input_2d(self):
master_active = []
try:
for x in range(0, self.hd):
active = []
#print(self.outputs)
for y in range(0, self.outputs):
sym_data = self.hs.hist_shaped[y][self.end_idx - x]
#print(len(sym_data))
active += sym_data.tolist()
master_active.append(active)
except:
self.refresh_data()
self.get_one_bar_input_2d()
#print(active)
return master_active
def poloTrader(self):
try:
trade_df = pd.read_json("./live_hist/json_hist.json")
except Exception as e:
trade_df = pd.DataFrame()
end_prices = {}
active = self.get_one_bar_input_2d()
self.load_net()
sub = Substrate(self.in_shapes, self.out_shapes)
net = ESNetwork(sub, self.cppn, self.params, self.hd)
network = net.create_phenotype_network_nd('paper_net.png')
sell_syms = []
buy_syms = []
buy_signals = []
sell_signals = []
for n in range(1, self.hd):
network.activate(active[self.hd - n])
out = network.activate(active[0])
self.reset_tickers()
for x in range(len(out)):
sym = self.hs.coin_dict[x]
end_prices[sym] = self.get_price(self.base_sym + "_" + sym)
if (out[x] > .5):
buy_signals.append(out[x])
buy_syms.append(sym)
if (out[x] < -.5):
sell_signals.append(out[x])
sell_syms.append(sym)
#rng = iter(shuffle(rng))
sorted_buys = np.argsort(buy_signals)[::-1]
sorted_sells = np.argsort(sell_signals)
for x in sorted_sells:
try:
sym = sell_syms[x]
p = end_prices[sym]
print("selling: ", sym)
self.folio.sell_coin(sym, p)
except Exception as e:
print("error placing order")
for x in sorted_buys:
try:
sym = buy_syms[x]
p = end_prices[sym]
print("buying: ", sym)
self.folio.buy_coin(sym, p)
except Exception as e:
print("error placing order")
'''
self.trade_hist["date"] = datetime.now()
self.trade_hist["portfoliovalue"] = self.folio.get_total_btc_value_no_sell(end_prices)[0]
self.trade_hist["portfolio"] = self.folio.ledger
self.trade_hist["percentchange"] = ((self.trade_hist["portfoliovalue"] - self.folio.start)/self.folio.start)*100
trade_df.append(self.trade_hist)
trade_df.to_json("./live_hist/json_hist.json")
if(self.trade_hist["portfoliovalue"] > self.folio.start *1.1):
self.folio.start = self.folio.get_total_btc_value(end_prices)[0]
'''
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(2):
p_vals = self.get_current_balance()
print("current value: ", p_vals[0], "current holdings: ",
p_vals[1])
time.sleep(self.ticker_len / 2)
self.refresh_data()
self.poloTrader()
class PaperTrader:
params = {"initial_depth": 3,
"max_depth": 4,
"variance_threshold": 0.00013,
"band_threshold": 0.00013,
"iteration_level": 3,
"division_threshold": 0.00013,
"max_weight": 3.0,
"activation": "tanh"}
in_shapes = []
out_shapes = []
db = TinyDB('./live_hist/memories.json')
# Config for CPPN.
config = neat.config.Config(neat.genome.DefaultGenome, neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet, neat.stagnation.DefaultStagnation,
'config_trader')
def __init__(self, ticker_len, start_amount, histdepth):
self.trade_hist = {}
self.polo = Poloniex()
self.hist_depth = histdepth
self.ticker_len = ticker_len
self.end_ts = datetime.now()+timedelta(seconds=(ticker_len*89))
self.start_amount = start_amount
self.hs = HistWorker()
self.refresh_data()
self.folio = CryptoFolio(start_amount, list(self.hs.currentHists.keys()))
self.inputs = self.hs.hist_shaped.shape[0]*(self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
self.leaf_names = []
#num_leafs = 2**(len(self.node_names)-1)//2
self.tree = nDimensionGoldenTree((0.0, 0.0, 0.0), 1.0, 1)
self.tree.divide_childrens()
self.set_substrate()
self.set_leaf_names()
self.epoch_len = histdepth
self.load_net()
print(self.hs.coin_dict)
self.db.purge()
self.poloTrader()
def set_leaf_names(self):
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_substrate(self):
sign = 1
x_increment = 1.0 / self.outputs
y_increment = 1.0 / len(self.hs.hist_shaped[0][0])
for ix in range(self.outputs):
self.out_shapes.append((1.0-(ix*x_increment), 0.0, -1.0))
for ix2 in range(self.inputs//self.outputs):
if(ix2 >= len(self.tree.cs)-1):
treex = ix2 - len(self.tree.cs)-1
else:
treex = ix2
center = self.tree.cs[treex]
self.in_shapes.append((center.coord[0]+(ix*x_increment), center.coord[1] - (ix2*y_increment), center.coord[2]+.5))
self.subStrate = Substrate(self.in_shapes, self.out_shapes)
def set_portfolio_keys(self, folio):
for k in self.hs.currentHists.keys():
folio.ledger[k] = 0
def refresh_data(self):
self.hs.pull_polo_live(21)
self.hs.combine_live_frames(89)
def load_net(self):
#file = open("./champ_gens/thot-checkpoint-13",'rb')
g = neat.Checkpointer.restore_checkpoint("./binance_champs_2/tradegod-checkpoint-48")
'''
best_fit = 0.0
for gx in g.population:
if g.population[gx].fitness != None:
if g.population[gx].fitness > best_fit:
bestg = g.population[gx]
g = bestg
'''
#file.close()
g = g.population[5910]
[the_cppn] = create_cppn(g, self.config, self.leaf_names, ['cppn_out'])
self.cppn = the_cppn
def make_shapes(self):
self.in_shapes = []
self.out_shapes = []
sign = 1
for ix in range(1,self.outputs+1):
sign = sign *-1
self.out_shapes.append((0.0-(sign*.005*ix), -1.0, -1.0))
for ix2 in range(1,(self.inputs//self.outputs)+1):
self.in_shapes.append((0.0+(sign*.01*ix2), 0.0-(sign*.01*ix2), 1.0))
def reset_tickers(self):
try:
self.tickers = self.polo.returnTicker()
except:
time.sleep(360)
self.reset_tickers()
return
def get_price(self, coin):
return self.tickers[coin]['last']
def get_current_balance(self):
#self.refresh_data()
c_prices = {}
for s in self.hs.currentHists.keys():
if s != 'BTC':
c_prices[s] = self.hs.currentHists[s]['close'].iloc[-1]
return self.folio.get_total_btc_value_no_sell(c_prices)
def get_one_bar_input_2d(self,end_idx=10):
master_active = []
for x in range(0, self.hist_depth):
active = []
#print(self.outputs)
for y in range(0, self.outputs):
sym_data = self.hs.hist_shaped[y][self.hist_depth-x]
#print(len(sym_data))
active += sym_data.tolist()
master_active.append(active)
#print(active)
return master_active
def poloTrader(self):
try:
trade_df = pd.read_json("./live_hist/json_hist.json")
except:
trade_df = pd.DataFrame()
end_prices = {}
active = self.get_one_bar_input_2d()
self.load_net()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network_nd()
net.reset()
signals = []
net.reset()
for n in range(1, self.hist_depth+1):
out = net.activate(active[self.hist_depth-n])
for x in range(len(out)):
signals.append(out[x])
#rng = iter(shuffle(rng))
sorted_shit = np.argsort(signals)[::-1]
self.reset_tickers()
sym = ""
for x in sorted_shit:
sym = self.hs.coin_dict[x]
#print(out[x])
try:
if(out[x] < -.5):
p = self.get_price('BTC_'+sym)
print("selling: ", sym)
self.folio.sell_coin(sym, p)
elif(out[x] > .5):
p = self.get_price('BTC_'+sym)
print("buying: ", sym)
self.folio.buy_coin(sym, p)
except:
print("error buying or selling")
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hs.currentHists[sym]["close"].iloc[-1]
#self.trade_hist["date"] = self.hs.currentHists[sym]["date"].iloc[-1]
self.trade_hist["date"] = time.time()
self.trade_hist["portfoliovalue"] = self.folio.get_total_btc_value_no_sell(end_prices)[0]
self.trade_hist["portfolio"] = self.folio.ledger
self.trade_hist["percentchange"] = ((self.trade_hist["portfoliovalue"] - self.folio.start)/self.folio.start)*100
print(self.trade_hist)
print(self.folio.ledger)
self.db.insert(self.trade_hist)
self.trade_hist = {}
'''
if(self.trade_hist["portfoliovalue"] > self.folio.start *1.1):
self.folio.start = self.folio.get_total_btc_value(end_prices)[0]
'''
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(2):
time.sleep(self.ticker_len/2)
#print(self.folio.ledger)
self.refresh_data
self.poloTrader()
class PaperTrader:
params = {
"initial_depth": 0,
"max_depth": 4,
"variance_threshold": 0.03,
"band_threshold": 0.3,
"iteration_level": 1,
"division_threshold": 0.3,
"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')
def __init__(self, ticker_len, start_amount):
self.polo = Poloniex()
self.currentHists = {}
self.hist_shaped = {}
self.coin_dict = {}
self.ticker_len = ticker_len
self.end_ts = datetime.now() + timedelta(seconds=(ticker_len * 24))
self.start_amount = start_amount
file = open("es_trade_god_cppn.pkl", 'rb')
self.cppn = pickle.load(file)
file.close()
self.pull_polo()
self.inputs = self.hist_shaped.shape[0] * (
self.hist_shaped[0].shape[1] - 1)
self.outputs = self.hist_shaped.shape[0]
self.multiplier = self.inputs / self.outputs
self.folio = CryptoFolio(start_amount, self.coin_dict)
def make_shapes(self):
self.in_shapes = []
self.out_shapes = []
sign = 1
for ix in range(self.outputs):
sign = sign * -1
self.out_shapes.append((sign * ix, 1))
for ix2 in range(len(self.hist_shaped[0][0]) - 1):
self.in_shapes.append((sign * ix, (1 + ix2) * .1))
def pull_polo(self):
try:
self.coins = self.polo.returnTicker()
except:
time.sleep(10)
self.pull_polo()
tickLen = '7200'
start = datetime.today() - timedelta(1)
start = str(int(start.timestamp()))
ix = 0
for coin in self.coins:
if coin[:3] == 'BTC':
hist = requests.get(
'https://poloniex.com/public?command=returnChartData¤cyPair='
+ coin + '&start=' + start + '&end=9999999999&period=' +
tickLen)
try:
df = pd.DataFrame(hist.json())
#df.rename(columns = lambda x: col_prefix+'_'+x, inplace=True)
as_array = np.array(df)
#print(len(as_array))
self.currentHists[coin] = df
self.hist_shaped[ix] = as_array
self.coin_dict[ix] = coin
ix += 1
except:
print("error reading json")
self.hist_shaped = pd.Series(self.hist_shaped)
self.end_idx = len(self.hist_shaped[0]) - 1
def get_current_balance(self):
self.pull_polo()
c_prices = {}
for s in self.folio.ledger.keys():
if s != 'BTC':
c_prices[s] = self.currentHists[s]['close'][
len(self.currentHists[s]['close']) - 1]
return self.folio.get_total_btc_value_no_sell(c_prices)
def get_one_bar_input_2d(self):
active = []
misses = 0
for x in range(0, self.outputs):
try:
sym_data = self.hist_shaped[x][self.end_idx]
for i in range(len(sym_data)):
if (i != 1):
active.append(sym_data[i].tolist())
except:
self.outputs -= 1
self.inputs -= self.multiplier
print('error')
#print(active)
self.make_shapes()
return active
def poloTrader(self):
end_prices = {}
active = self.get_one_bar_input_2d()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network()
net.reset()
for n in range(network.activations):
out = net.activate(active)
#print(len(out))
rng = len(out)
#rng = iter(shuffle(rng))
for x in np.random.permutation(rng):
sym = self.coin_dict[x]
#print(out[x])
try:
if (out[x] < -.5):
print("selling: ", sym)
self.folio.sell_coin(
sym, self.currentHists[sym]['close'][self.end_idx])
elif (out[x] > .5):
print("buying: ", sym)
self.folio.buy_coin(
sym, self.currentHists[sym]['close'][self.end_idx])
except:
print('error', sym)
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hist_shaped[x][len(self.hist_shaped[x]) -
1][2]
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(3):
time.sleep(self.ticker_len / 4)
p_vals = self.get_current_balance()
print("current value: ", p_vals[0], "current btc holdings: ",
p_vals[1])
#print(self.folio.ledger)
time.sleep(self.ticker_len / 4)
self.pull_polo()
self.poloTrader()
def evaluate(self, network, es, rand_start, g, p_name):
portfolio_start = 1.0
portfolio = CryptoFolio(portfolio_start,
list(self.hs.currentHists.keys()))
end_prices = {}
port_ref = portfolio_start
with open('./champs_histd3/port_hist' + str(p_name) + '.txt',
'w') as pt:
pt.write('date,current_balance \n')
with open('./champs_histd3/trade_hist' + str(p_name) + '.txt',
'w') as ft:
ft.write('date,symbol,type,amnt,price,current_balance \n')
for z in range(34, self.hs.hist_full_size - 1):
active = self.get_one_epoch_input(z)
signals = []
network.reset()
for n in range(1, self.hd + 1):
out = network.activate(active[self.hd - n])
for x in range(len(out)):
signals.append(out[x])
sym2 = list(self.hs.currentHists.keys())[x]
end_prices[sym2] = self.hs.currentHists[sym2]['close'][
z]
sorted_shit = np.argsort(signals)[::-1]
rebalance = portfolio_start
#rng = iter(shuffle(rng))
sym = ""
for x in sorted_shit:
sym = list(self.hs.currentHists.keys())[x]
#print(out[x])
#try:
if (out[x] < -.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[x] > .5):
did_buy = portfolio.buy_coin(
sym, self.hs.currentHists[sym]['close'][z])
if did_buy:
portfolio.target_amount = .1 + (out[x] * .1)
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")
#else:
pt.write(str(self.hs.currentHists[sym]['date'][z]) + ",")
#pt.write(str(self.hs.currentHists[sym]['close'][z])+",")
pt.write(
str(
portfolio.get_total_btc_value_no_sell(end_prices)
[0]) + " \n")
#print("sold ", sym)
new_ref = portfolio.get_total_btc_value_no_sell(
end_prices)[0]
'''
if(new_ref > 1.10 * port_ref or new_ref < .9*port_ref):
port_ref = portfolio.get_total_btc_value_no_sell(end_prices)[0]
portfolio.start = port_ref
#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)
fit = result_val[0]
return fit
class PaperTrader:
params = {"initial_depth": 3,
"max_depth": 4,
"variance_threshold": 0.00013,
"band_threshold": 0.0000013,
"iteration_level": 3,
"division_threshold": 0.00013,
"max_weight": 8.0,
"activation": "tanh"}
# Config for CPPN.
config = neat.config.Config(neat.genome.DefaultGenome, neat.reproduction.DefaultReproduction,
neat.species.DefaultSpeciesSet, neat.stagnation.DefaultStagnation,
'config_trader')
def __init__(self, ticker_len, start_amount, histdepth):
self.in_shapes = []
self.out_shapes = []
self.trade_hist = {}
self.polo = Poloniex()
self.hist_depth = histdepth
self.ticker_len = ticker_len
self.end_ts = datetime.now()+timedelta(seconds=(ticker_len*24))
self.start_amount = start_amount
self.hs = HistWorker()
self.refresh_data()
self.inputs = self.hs.hist_shaped.shape[0]*(self.hs.hist_shaped[0].shape[1])
self.outputs = self.hs.hist_shaped.shape[0]
#self.make_shapes()
self.folio = CryptoFolio(start_amount, list(self.hs.currentHists.keys()))
self.leaf_names = []
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)
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.load_net()
print(self.hs.coin_dict)
self.poloTrader()
def refresh_data(self):
self.hs.pull_polo_live(12*30)
self.hs.combine_live_frames(12*30)
def load_net(self):
#file = open("./champ_gens/thot-checkpoint-13",'rb')
g = neat.Checkpointer.restore_checkpoint("./champ_gens/thot-checkpoint-25")
best_fit = 0.0
for gx in g.population:
if g.population[gx].fitness != None:
if g.population[gx].fitness > best_fit:
bestg = g.population[gx]
g = bestg
#file.close()
[the_cppn] = create_cppn(g, self.config, self.leaf_names, ['cppn_out'])
self.cppn = the_cppn
def make_shapes(self):
self.in_shapes = []
self.out_shapes = []
sign = 1
for ix in range(1,self.outputs+1):
sign = sign *-1
self.out_shapes.append((0.0-(sign*.005*ix), -1.0, -1.0))
for ix2 in range(1,(self.inputs//self.outputs)+1):
self.in_shapes.append((0.0+(sign*.01*ix2), 0.0-(sign*.01*ix2), 1.0))
def reset_tickers(self):
try:
self.tickers = self.polo.returnTicker()
except:
time.sleep(360)
self.reset_tickers()
return
def get_price(self, coin):
return self.tickers[coin]['last']
def get_current_balance(self):
#self.refresh_data()
c_prices = {}
for s in self.hs.currentHists.keys():
if s != 'BTC':
c_prices[s] = self.hs.currentHists[s]['close'].iloc[-1]
return self.folio.get_total_btc_value_no_sell(c_prices)
def get_one_bar_input_2d(self,end_idx=10):
master_active = []
for x in range(0, self.hist_depth):
active = []
#print(self.outputs)
for y in range(0, self.outputs):
sym_data = self.hs.hist_shaped[y][self.hist_depth-x]
#print(len(sym_data))
active += sym_data.tolist()
master_active.append(active)
#print(active)
return master_active
def poloTrader(self):
try:
trade_df = pd.read_json("./live_hist/json_hist.json")
except:
trade_df = pd.DataFrame()
end_prices = {}
active = self.get_one_bar_input_2d()
self.load_net()
sub = Substrate(self.in_shapes, self.out_shapes)
network = ESNetwork(sub, self.cppn, self.params)
net = network.create_phenotype_network_nd('paper_net.png')
net.reset()
for n in range(1, self.hist_depth+1):
out = net.activate(active[self.hist_depth-n])
#print(len(out))
rng = len(out)
#rng = iter(shuffle(rng))
self.reset_tickers()
for x in np.random.permutation(rng):
sym = self.hs.coin_dict[x]
#print(out[x])
try:
if(out[x] < -.5):
p = self.get_price('BTC_'+sym)
print("selling: ", sym)
self.folio.sell_coin(sym, p)
elif(out[x] > .5):
p = self.get_price('BTC_'+sym)
print("buying: ", sym)
self.folio.buy_coin(sym, p)
else:
except:
print("error buying or selling")
#skip the hold case because we just dont buy or sell hehe
end_prices[sym] = self.hs.currentHists[sym]["close"].iloc[-1]
self.trade_hist["date"] = datetime.now()
self.trade_hist["portfoliovalue"] = self.folio.get_total_btc_value_no_sell(end_prices)[0]
self.trade_hist["portfolio"] = self.folio.ledger
self.trade_hist["percentchange"] = ((self.trade_hist["portfoliovalue"] - self.folio.start)/sefl.folio.start)*100
trade_df.append(self.trade_hist)
trade_df.to_json("./live_hist/json_hist.json")
'''
if(self.trade_hist["portfoliovalue"] > self.folio.start *1.1):
self.folio.start = self.folio.get_total_btc_value(end_prices)[0]
'''
if datetime.now() >= self.end_ts:
port_info = self.folio.get_total_btc_value(end_prices)
print("total val: ", port_info[0], "btc balance: ", port_info[1])
return
else:
print(self.get_current_balance())
for t in range(2):
self.refresh_data
p_vals = self.get_current_balance()
print("current value: ", p_vals[0], "current btc holdings: ", p_vals[1])
time.sleep(self.ticker_len/2)
#print(self.folio.ledger)
self.poloTrader()
def evaluate(self, network, es, rand_start, g, verbose=False):
portfolio_start = 10000.0
portfolio = CryptoFolio(portfolio_start, self.hs.coin_dict, "USDT")
end_prices = {}
buys = 0
sells = 0
loss_factor = 0
for z_minus in range(0, self.epoch_len - 1):
#TODO add comments to clarify all the
#shit im doing here
z = rand_start - z_minus
active = self.get_net_input(z)
buy_signals = []
buy_syms = []
sell_syms = []
sell_signals = []
network.reset()
for n in range(0, es.activations):
out = network.activate(active)
for x in range(len(out)):
if x > 0:
if (portfolio.get_total_btc_value_no_sell(end_prices)[0] <
portfolio_start):
loss_factor += .005
sym = self.hs.coin_dict[x]
if (out[x] > .5):
#print("buying " + sym)
portfolio.buy_coin(
sym, self.hs.currentHists[sym]['open_price'][z])
if (out[x] < -.5):
#print("selling " + sym)
portfolio.sell_coin(
sym, self.hs.currentHists[sym]['open_price'][z])
end_prices[sym] = self.hs.currentHists[sym]['open_price'][z]
'''
# if this is the last loop of bars
if(z > (self.epoch_len+rand_start)-2):
sym = self.hs.coin_dict[x]
end_prices[sym] = self.hs.currentHists[sym]['open_price'][self.epoch_len+rand_start]
if(out[x] > .5):
buy_signals.append(out[x])
buy_syms.append(self.hs.coin_dict[x])
if(out[x] < -.5):
sell_signals.append(out[x])
sell_syms.append(self.hs.coin_dict[x])
#rng = iter(shuffle(rng))
sorted_buys = np.argsort(buy_signals)[::-1]
sorted_sells = np.argsort(sell_signals)
#print(len(sorted_shit), len(key_list))
for x in sorted_sells:
sym = sell_syms[x]
portfolio.sell_coin(sym, self.hs.currentHists[sym]['open_price'][z])
for x in sorted_buys:
sym = buy_syms[x]
#portfolio.target_amount = .1 + (out[x] * .1)
portfolio.buy_coin(sym, self.hs.currentHists[sym]['open_price'][z])
'''
result_val = portfolio.get_total_btc_value(end_prices)
print("genome id ", g.key, " : ")
print(result_val[0], "buys: ", result_val[1], "sells: ", result_val[2])
if result_val[1] == 0:
ft = .5
if result_val[2] > 2:
ft = (result_val[0] * 1.02) - loss_factor
else:
ft = result_val[0] - loss_factor
return ft