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")