def _create_wallet_source(wallet: 'Wallet', include_worth: bool = True) -> 'List[Stream[float]]': """Creates a list of streams to describe a `Wallet`. Parameters ---------- wallet : `Wallet` The wallet to make streams for. include_worth : bool, default True Whether or Returns ------- `List[Stream[float]]` A list of streams to describe the `wallet`. """ exchange_name = wallet.exchange.name symbol = wallet.instrument.symbol streams = [] with NameSpace(exchange_name + ":/" + symbol): free_balance = Stream.sensor(wallet, lambda w: w.balance.as_float(), dtype="float").rename("free") locked_balance = Stream.sensor(wallet, lambda w: w.locked_balance.as_float(), dtype="float").rename("locked") total_balance = Stream.sensor(wallet, lambda w: w.total_balance.as_float(), dtype="float").rename("total") streams += [free_balance, locked_balance, total_balance] if include_worth: price = Stream.select(wallet.exchange.streams(), lambda node: node.name.endswith(symbol)) worth = price.mul(total_balance).rename('worth') streams += [worth] return streams
def create_env(envs_config=None): features = [] for c in data.columns[5:]: s = Stream.source(list(data[c][-100:]), dtype="float").rename(data[c].name) features += [s] cp = Stream.select(features, lambda s: s.name == "close") features = [cp.log().diff().fillna(0).rename("lr")] + features[1:] feed = DataFeed(features) feed.compile() bitstamp = Exchange("bitstamp", service=execute_order)(Stream.source( list(data["close"]), dtype="float").rename("USD-BTC")) portfolio = Portfolio( USD, [Wallet(bitstamp, 10000 * USD), Wallet(bitstamp, 10 * BTC)]) renderer_feed = DataFeed([ Stream.source(list(data["date"])).rename("date"), Stream.source(list(data["open"]), dtype="float").rename("open"), Stream.source(list(data["high"]), dtype="float").rename("high"), Stream.source(list(data["low"]), dtype="float").rename("low"), Stream.source(list(data["close"]), dtype="float").rename("close"), Stream.source(list(data["volume"]), dtype="float").rename("volume") ]) # reward_scheme = RiskAdjustedReturns( # return_algorithm='sortino', # risk_free_rate=0.025, # target_returns=0.1, # window_size=200 # ) reward_scheme = SimpleProfit(window_size=200) #PBR(price=cp) # action_scheme = BSH( # cash=portfolio.wallets[0], # asset=portfolio.wallets[1] # ).attach(reward_scheme) env = default.create( portfolio=portfolio, action_scheme="managed-risk", reward_scheme=reward_scheme, #"risk-adjusted", feed=feed, renderer_feed=renderer_feed, renderer=default.renderers.PlotlyTradingChart(display=False, save_format='html', path='./agents/charts/'), window_size=40) return env
def _setup_stream(self): """ Sets up data stream with indicators and close :return: tensortrade.feed.core.DataFeed """ features = [] for c in self._data.columns[1:]: s = Stream.source(list(self._data[c]), dtype="float").rename(self._data[c].name) features += [s] cp = Stream.select(features, lambda s: s.name == "close") features = [ cp.log().diff().rename("lr"), rsi(cp, period=20).rename("rsi"), macd(cp, fast=10, slow=50, signal=5).rename("macd") ] feed = DataFeed(features) feed.compile() return feed
def macd(price: Stream[float], fast: float, slow: float, signal: float) -> Stream[float]: fm = price.ewm(span=fast, adjust=False).mean() sm = price.ewm(span=slow, adjust=False).mean() md = fm - sm signal = md - md.ewm(span=signal, adjust=False).mean() return signal features = [] for c in data.columns[1:]: s = Stream.source(list(data[c]), dtype="float").rename(data[c].name) features += [s] cp = Stream.select(features, lambda s: s.name == "close") op = Stream.select(features, lambda s: s.name == 'open') hp = Stream.select(features, lambda s: s.name == 'high') lp = Stream.select(features, lambda s: s.name == 'low') v = Stream.select(features, lambda s: s.name == 'volume') features = [ cp.log().diff().rename("lr"), op.log().diff().rename("op"), hp.log().diff().rename("hp"), lp.log().diff().rename("lp"), v.log().diff().rename("v"), rsi(cp, period=20).rename("rsi"), macd(cp, fast=10, slow=50, signal=5).rename("macd"), ]
def build_env(config): worker_index = 1 if hasattr(config, 'worker_index'): worker_index = config.worker_index raw_data = pd.read_csv(btc_usd_file, sep=';') raw_data['date'] = pd.to_datetime(raw_data['time'], unit='ms') data = compute_features(raw_data) features = [] for c in data.columns: if c not in raw_data.columns: s = Stream.source(list(data[c]), dtype="float").rename(data[c].name) features += [s] comm = 0.00001 coinbase = Exchange("coinbase", service=execute_order, options=ExchangeOptions(commission=comm))( Stream.source(list(data["close"]), dtype="float").rename("USD-BTC")) cash = Wallet(coinbase, 10000 * USD) asset = Wallet(coinbase, 0 * BTC) portfolio = Portfolio(USD, [cash, asset]) renderer_feed = DataFeed([ Stream.source(list(data["date"])).rename("date"), Stream.source(list(data["open"]), dtype="float").rename("open"), Stream.source(list(data["high"]), dtype="float").rename("high"), Stream.source(list(data["low"]), dtype="float").rename("low"), Stream.source(list(data["close"]), dtype="float").rename("close"), Stream.source(list(data["volume"]), dtype="float").rename("volume") ]) # reward_scheme = rewards.SimpleProfit() rsi = Stream.select(features, lambda x: x.name == "rsi") reward_scheme = SparseReward(rsi=rsi, window_size=10) action_scheme = BuySellHoldActionSchemes(cash, asset) action_scheme.attach(reward_scheme) plotly = PlotlyTradingChart(display=True, height=700, save_format="html") class EpisodeStopper(Stopper): def stop(self, env: 'TradingEnv') -> bool: return env.clock.num_steps > 1000 open_position = Stream.sensor(asset, lambda a: asset.total_balance.as_float() > 0) # open_position = Stream.sensor( # action_scheme, lambda action_scheme: action_scheme.has_asset # ) features.append(open_position) feed = DataFeed(features) feed.compile() env = default.create( portfolio=portfolio, action_scheme=action_scheme, reward_scheme=reward_scheme, feed=feed, renderer_feed=renderer_feed, renderer=plotly, window_size=20, max_allowed_loss=0.5, stopper=EpisodeStopper(), callback=(LoggingCallback('http://165.227.193.153:8050', plotly) if worker_index == 1 else None)) import logging import os LOGGER = logging.getLogger(__name__) logging.basicConfig( level=logging.INFO, format= '%(asctime)s - %(name)s [%(threadName)s] - %(levelname)s - %(message)s', ) LOGGER.info('env created logger') LOGGER.info(f'env: {os.environ}') print(f'env: {os.environ}') print('env created') return env
def create_env(data, config): features = [] for c in data.columns[1:]: s = Stream.source(list(data[c]), dtype="float").rename(data[c].name) features += [s] cp = Stream.select(features, lambda s: s.name == "close") high_price = data['high'] low_price = data['low'] close_price = data['close'] # print(data['volume']) try: data['date'] except KeyError: data['date'] = data.index features = [ cp.rename('USD/BTC'), cp.log().diff().rename("lr"), rsi(cp, period=14).rename("rsi"), macd(cp, fast=10, slow=50, signal=5).rename("macd"), Stream.source(ta.cci(high_price, low_price, close_price)).rename('cci') # cp.rolling(window=10).mean().rename("fast"), # cp.rolling(window=50).mean().rename("medium"), # cp.rolling(window=100).mean().rename("slow") ] feed = DataFeed(features) feed.compile() # for i in range(5): # print(feed.next()) coinbase = Exchange("coinbase", service=execute_order)(cp) cash = Wallet(coinbase, 100000 * USD) asset = Wallet(coinbase, 10 * BTC) portfolio = Portfolio(USD, [cash, asset]) # reward_scheme = PBR(price=cp) reward_scheme = RiskAdjustedReturns() # action_scheme = BSH(cash=cash, asset=asset).attach(reward_scheme) action_scheme = ManagedRiskOrders() renderer_feed = DataFeed([ Stream.source(list(data["date"])).rename("date"), Stream.source(list(data["open"]), dtype="float").rename("open"), Stream.source(list(data["high"]), dtype="float").rename("high"), Stream.source(list(data["low"]), dtype="float").rename("low"), Stream.source(list(data["close"]), dtype="float").rename("close"), Stream.source(list(data["volume"]), dtype="float").rename("volume"), #Stream.sensor(action_scheme, lambda s: s.action, dtype="float").rename("action") ]) renderer = PlotlyTradingChart() environment = default.create( feed=feed, portfolio=portfolio, action_scheme=action_scheme, # The DQN example uses action_scheme="managed-risk" reward_scheme=reward_scheme, # The DQN uses reward_scheme="risk-adjusted" renderer_feed=renderer_feed, renderer=renderer, window_size=config["window_size"], max_allowed_loss=0.6 ) return environment