from hist_service import HistWorker hs = HistWorker() hs.combine_polo_frames_vol_sorted(3) print(next(iter(hs.currentHists.values())).head()) hs.currentHists = {} hs.combine_binance_frames_vol_sorted(3) print(next(iter(hs.currentHists.values())).head())
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_trader0') start_idx = 0 highest_returns = 0 portfolio_list = [] in_shapes = [] out_shapes = [] def __init__(self, hist_depth): self.hs = HistWorker() self.hs.combine_polo_frames_vol_sorted() 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.shape[0] * ( self.hs.hist_shaped[0].shape[1]) self.outputs = self.hs.hist_shaped.shape[0] sign = 1 for ix in range(1, self.outputs + 1): sign = sign * -1 self.out_shapes.append( (-1.0, 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), 0.0 + (sign * (self.hd / self.hd + ix + ix2)), 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 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 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]) #print(sorted_shit, len(sorted_shit)) #print(len(sorted_shit), len(key_list)) for x in range(len(out)): 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'][ 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]) if (result_val[1] == 0): ft = result_val[0] / 2 else: 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): self.epoch_len = randint(255, 455) r_start = randint(0 + self.hd, self.hs.hist_full_size - self.epoch_len) for idx, g in genomes: cppn = neat.nn.FeedForwardNetwork.create(g, config) network = ESNetwork(self.subStrate, cppn, self.params) net = network.create_phenotype_network_nd() g.fitness = self.evaluate(net, network, r_start, g) return