def create_env(config, train="train"):
cdd = CryptoDataDownload()
data = cdd.fetch("Bitstamp", "USD", "BTC", "1h")
if False:
data.close = data.close / 20 + range(len(data))
print("genenrating fake increase")
if train == "train":
data = data[0:int(len(data) / 2)] # training
print("using first half for training")
elif train == "eval":
data = data[int(len(data) / 2):] # validation
print("using second half for eval")
else:
print("using all data")
pclose = Stream.source(list(data.close), dtype="float").rename("USD-BTC")
pmin = Stream.source(list(data.low), dtype="float").rename("USD-BTClow")
pmax = Stream.source(list(data.high), dtype="float").rename("USD-BTChigh")
pmin = Stream.source(list(data.low), dtype="float").rename("USD-BTClow")
pmax = Stream.source(list(data.high), dtype="float").rename("USD-BTChigh")
pmin3 = pmin.rolling(window=3).min()
pmin10 = pmin.rolling(window=10).min()
pmin20 = pmin.rolling(window=20).min()
pmax3 = pmax.rolling(window=3).max()
pmax10 = pmax.rolling(window=10).max()
pmax20 = pmax.rolling(window=20).max()
eo = ExchangeOptions(commission=0.002) #
coinbase = Exchange("coinbase", service=execute_order, options=eo)(
pclose
)
cash = Wallet(coinbase, 100000 * USD)
asset = Wallet(coinbase, 0 * BTC)
portfolio = Portfolio(USD, [
cash,
asset
])
feed = DataFeed([
(pclose.log() - pmin3.log()).fillna(0).rename("relmin3"),
(pclose.log() - pmin10.log()).fillna(0).rename("relmin10"),
(pclose.log() - pmin20.log()).fillna(0).rename("relmin20"),
(pclose.log() - pmax3.log()).fillna(0).rename("relmax3"),
(pclose.log() - pmax10.log()).fillna(0).rename("relmax10"),
(pclose.log() - pmax20.log()).fillna(0).rename("relmax20"),
])
action_scheme = BSH(cash=cash, asset=asset)
renderer_feed = DataFeed([
Stream.source(list(data.close), dtype="float").rename("price"),
Stream.sensor(action_scheme, lambda s: s.action, dtype="float").rename("action") # only works for BSH
])
environment = default.create(
feed=feed,
portfolio=portfolio,
action_scheme=action_scheme,
reward_scheme="simple",
renderer_feed=renderer_feed,
renderer=PositionChangeChart(),
window_size=config["window_size"],
min_periods=20,
max_allowed_loss=0.6
)
return environment
# %%
import sys
sys.path.append("/Users/fodrh1201/Workspace/tensortrade")
import pandas as pd
import tensortrade.env.default as default
from tensortrade.data.cdd import CryptoDataDownload
from tensortrade.feed.core import Stream, DataFeed
from tensortrade.oms.exchanges import Exchange
from tensortrade.oms.services.execution.simulated import execute_order
from tensortrade.oms.instruments import USD, BTC, ETH
from tensortrade.oms.wallets import Wallet, Portfolio
from tensortrade.agents import DQNAgent
# %%
cdd = CryptoDataDownload()
data = cdd.fetch("Bitstamp", "USD", "BTC", "1h")
# %%
def rsi(price: Stream[float], period: float) -> Stream[float]:
r = price.diff()
upside = r.clamp_min(0).abs()
downside = r.clamp_max(0).abs()
rs = upside.ewm(alpha=1 / period).mean() / downside.ewm(alpha=1 /
period).mean()
return 100 * (1 - (1 + rs)**-1)
def macd(price: Stream[float], fast: float, slow: float,
sys.path.insert(0, os.path.abspath("../../tensortrade"))
import tensortrade.env.default as default
from tensortrade.feed.core import Stream, DataFeed
from tensortrade.data.cdd import CryptoDataDownload
from tensortrade.oms.wallets import Portfolio, Wallet
from tensortrade.oms.exchanges import Exchange
from tensortrade.oms.services.execution.simulated import execute_order
from tensortrade.oms.instruments import USD, BTC, ETH, LTC
DOWNLOAD = False
if DOWNLOAD:
cdd = CryptoDataDownload()
bitstamp_btc = cdd.fetch("Bitstamp", "USD", "BTC", "1h")
bitstamp_eth = cdd.fetch("Bitstamp", "USD", "ETH", "1h")
bitstamp_ltc = cdd.fetch("Bitstamp", "USD", "LTC", "1h")
bitstamp_btc.to_csv("data/bitstamp_btc_hourly.csv")
bitstamp_eth.to_csv("data/bitstamp_eth_hourly.csv")
bitstamp_ltc.to_csv("data/bitstamp_ltc_hourly.csv")
bitstamp_btc = pandas.read_csv("data/bitstamp_btc_hourly.csv")
bitstamp_eth = pandas.read_csv("data/bitstamp_eth_hourly.csv")
bitstamp_ltc = pandas.read_csv("data/bitstamp_ltc_hourly.csv")
# Inspec transactions of Simple orders
bitstamp = Exchange("bitstamp",
def create_recent_coinbase_env(config):
cdd = CryptoDataDownload()
data = cdd.fetch("gemini", "USD", "BTC", "1h")
return create_env(data, config)
import ta
import pandas as pd
from tensortrade.data.cdd import CryptoDataDownload
from tensortrade.feed.core import Stream, DataFeed, NameSpace
cdd = CryptoDataDownload()
def rsi(price: Stream[float], period: float) -> Stream[float]:
"""
Relative strength index
The relative strength index (RSI) is a momentum indicator used in technical analysis that measures the
magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or
other asset
:param price:
:param period:
:return:
"""
r = price.diff()
upside = r.clamp_min(0).abs()
downside = r.clamp_max(0).abs()
rs = upside.ewm(alpha=1 / period).mean() / downside.ewm(alpha=1 / period).mean()
return 100*(1 - (1 + rs) ** -1)
def macd(price: Stream[float], fast: float, slow: float, signal: float) -> Stream[float]:
"""
Moving Average Convergence Divergence
This technical indicator is a tool that’s used to identify moving averages that are indicating a new trend,
whether it’s bullish or bearish.
def create_env(config):
cdd = CryptoDataDownload()
# Head for beginning of data
# Tail for end of data
if config["train"] == True:
df = cdd.fetch("Bitfinex", "USD", "BTC", "d")[-600:-100]
else: #for testing
df = cdd.fetch("Bitfinex", "USD", "BTC", "d")[-100:]
price_history = df[['date', 'open', 'high', 'low', 'close', 'volume']] # chart data
# OBSERVER
p = Stream.source(price_history['close'].tolist(), dtype="float").rename("USD-BTC")
bitfinex = Exchange("bitfinex", service=execute_order)(
p
)
# ORDER MANAGEMENT SYSTEM
#start with 100.000 usd and 0 assets
cash = Wallet(bitfinex, 100000 * USD)
asset = Wallet(bitfinex, 0 * BTC)
portfolio = Portfolio(USD, [
cash,
asset
])
# OBSERVER
df.drop(columns=['date', 'open', 'high', 'low', 'close', 'volume'], inplace=True)
with NameSpace("bitfinex"):
streams = [Stream.source(df[c].tolist(), dtype="float").rename(c) for c in df.columns]
feed = DataFeed(streams)
# REWARDSCHEME
#use PBR as reward_scheme
#'SimpleProfit' object has no attribute 'on_action'
reward_scheme = PBR(price=p) #SimpleProfit()#RiskAdjustedReturns()
# ACTIONSCHEME
#use BSH as action_scheme
action_scheme = BSH(#ManagedRiskOrders()
cash=cash,
asset=asset
).attach(reward_scheme)
# RENDERER
renderer_feed = DataFeed(
[Stream.source(price_history[c].tolist(), dtype="float").rename(c) for c in price_history]
)
#create the environment
environment = default.create(
feed=feed,
portfolio=portfolio,
action_scheme=action_scheme,
reward_scheme=reward_scheme,
renderer_feed=renderer_feed,
renderer= PlotlyTradingChart(), #PositionChangeChart(), #
window_size=config["window_size"], #part of OBSERVER
max_allowed_loss=config["max_allowed_loss"] #STOPPER
)
return environment