├── .gitignore ├── LICENSE ├── README.md ├── backtrader_rl ├── __init__.py ├── adapters │ ├── __init__.py │ ├── gymAdapter.py │ └── tensorforceAdapter.py ├── engines.py ├── strategys.py └── utils.py ├── examples ├── BNB_USDT_5m.csv ├── BTC_USDT_1h.csv ├── BTC_USDT_5m.csv ├── downloader.py ├── gym_env_example.py ├── raylib_example.py └── tensorforce_example.py └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Backtrader-RL 2 | 3 | Reinforcemenet Learning Environment build upon the backtrader ecosystem 4 | 5 | # TODO 6 | 7 | * [X] Environment Wrapper to instead step through a cerebro.run call 8 | -------------------------------------------------------------------------------- /backtrader_rl/__init__.py: -------------------------------------------------------------------------------- 1 | name = "btrl" -------------------------------------------------------------------------------- /backtrader_rl/adapters/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaxLoedige/Backtrader-RL/028e227aa7e653e1159c5029fad1dc1bd3255664/backtrader_rl/adapters/__init__.py -------------------------------------------------------------------------------- /backtrader_rl/adapters/gymAdapter.py: -------------------------------------------------------------------------------- 1 | import gym 2 | from gym import spaces 3 | from numpy import infty 4 | 5 | class gymAdapter(gym.Env): 6 | 7 | def __init__(self,engine,**kwargs): 8 | super().__init__(**kwargs) 9 | self.engine = engine 10 | self.action_space = spaces.Discrete(3) 11 | self.engine.action_mapping = {"buy" : 2, "sell" : 0, "hold":1} 12 | self.observation_space = spaces.Box(low = 0,high = infty, shape = self.engine.state_shape) 13 | 14 | def step(self,action): 15 | observation, reward, self.terminated = self.engine.step(action) 16 | truncated = False 17 | info = {} 18 | return observation, reward, self.terminated, truncated, info 19 | 20 | def reset(self,seed = None, options = {}): 21 | observation = self.engine.reset(seed = seed,**options) 22 | info = {} 23 | return observation, info 24 | 25 | def render(self): 26 | if self.terminated: 27 | self.engine.plot() 28 | 29 | def close(self): 30 | self.engine.close() 31 | -------------------------------------------------------------------------------- /backtrader_rl/adapters/tensorforceAdapter.py: -------------------------------------------------------------------------------- 1 | from tensorforce import Environment 2 | from numpy import infty 3 | 4 | class tensorforceAdapter(Environment): 5 | 6 | def __init__(self,engine,**kwargs): 7 | super().__init__(**kwargs) 8 | self.engine = engine 9 | 10 | def states(self): 11 | return dict(type='float', min_value = 0, shape=self.engine.state_shape) 12 | 13 | def actions(self): 14 | return dict(type='int', num_values=len(self.engine.actions_mapping)) 15 | 16 | def close(self): 17 | self.engine.close() 18 | 19 | def reset(self,options = {}): 20 | observation = self.engine.reset(seed = None,**options) 21 | return observation 22 | 23 | def execute(self, actions): 24 | observation, reward, self.terminated = self.engine.step(actions) 25 | return observation, self.terminated, reward 26 | 27 | def plot(self): 28 | return self.engine.plot() -------------------------------------------------------------------------------- /backtrader_rl/engines.py: -------------------------------------------------------------------------------- 1 | import backtrader as bt 2 | from backtrader.utils import num2date, date2num 3 | import datetime 4 | import numpy as np 5 | 6 | class BTEngine(bt.Cerebro): 7 | 8 | params = ( 9 | ('preload', False), 10 | ('runonce', False), 11 | ('maxcpus', None), 12 | ('stdstats', False), 13 | ('oldbuysell', False), 14 | ('oldtrades', False), 15 | ('lookahead', 0), 16 | ('exactbars', False), 17 | ('optdatas', True), 18 | ('optreturn', True), 19 | ('objcache', False), 20 | ('live', False), 21 | ('writer', False), 22 | ('tradehistory', False), 23 | ('oldsync', False), 24 | ('tz', None), 25 | ('cheat_on_open', False), 26 | ('broker_coo', True), 27 | ('quicknotify', False), 28 | ) 29 | 30 | def __init__(self,lookback,state_rows = ("close",),action_mapping = None, normalize = True, canhold = True): 31 | super().__init__() 32 | 33 | if len(state_rows) == 1: 34 | 35 | if state_rows[0] == "ohlc": 36 | state_rows = ("open" , "high" , "close" , "low") 37 | 38 | elif state_rows[0] == "ohlcv": 39 | state_rows = ("open" , "high" , "close" , "low", "volume") 40 | 41 | if type(state_rows) == str: 42 | 43 | if state_rows == "ohlc": 44 | state_rows = ("open" , "high" , "close" , "low") 45 | 46 | elif state_rows == "ohlcv": 47 | state_rows = ("open" , "high" , "close" , "low", "volume") 48 | 49 | elif state_rows in ("open" , "high" , "close" , "low", "volume"): 50 | state_rows = (state_rows,) 51 | 52 | assert type(state_rows) == tuple 53 | 54 | 55 | self.state_shape = (len(state_rows),lookback) 56 | self.lookback = lookback 57 | 58 | self.state_rows = state_rows 59 | 60 | if canhold: 61 | self.num_actions = 3 62 | mapping ={"buy" : 2, "sell" : 0, "hold":1} 63 | else: 64 | self.num_actions = 2 65 | mapping ={"buy" : 1, "sell" : 0} 66 | 67 | if action_mapping: 68 | self.actions_mapping = action_mapping 69 | else: 70 | self.actions_mapping = mapping 71 | 72 | self.canhold = canhold 73 | 74 | self.possible_actions = [self.actions_mapping[key] for key in self.actions_mapping] 75 | self.possible_actions.append(-1) 76 | 77 | self.normalize = normalize 78 | 79 | self.broker.set_shortcash(False) 80 | 81 | ## ============================== 82 | ## Default Interface Methods 83 | ## ============================== 84 | 85 | def step(self,action = None): 86 | if action is None: 87 | # TODO define dault action 88 | action = -1 89 | 90 | assert action in self.possible_actions 91 | 92 | observation = None 93 | reward = 0 94 | #print(self.strats[0][0][0]) 95 | # self.strats[0][0][0]._setAction(action = action) 96 | 97 | for strat in self.runstrats_container: 98 | strat._setAction(action) 99 | 100 | self.terminated = self._step(self.runstrats_container,**self.bt_state_container) 101 | self.step_count += 1 102 | 103 | observation = self._get_observations() 104 | 105 | for strat in self.runstrats_container: 106 | reward = strat._computeReward() 107 | print(reward) 108 | 109 | return observation, reward, self.terminated 110 | 111 | def reset(self,**kwargs): 112 | self.step_count = 0 113 | self.terminated = False 114 | self.run(**kwargs) 115 | 116 | assert len(self.runstrats_container) == 1 117 | self.runstrats_container[0]._set_action_mapping(self.actions_mapping) 118 | 119 | observation = None 120 | 121 | # TODO fix starting period 122 | # kinda hacky skipping the first lookback 123 | for _ in range(self.lookback): 124 | observation = self.step()[0] 125 | if len(self.state_rows) == 1: 126 | if self.lookback == len(observation): 127 | break 128 | else: 129 | if self.lookback == observation.shape[1]: 130 | break 131 | 132 | return observation 133 | 134 | def close(self): 135 | # Last notification chance before stopping 136 | self._datanotify() 137 | if self._event_stop: # stop if requested 138 | return 139 | self._storenotify() 140 | if self._event_stop: # stop if requested 141 | return 142 | 143 | ## ============================== 144 | ## Cerebro Dissection Methods 145 | ## ============================== 146 | 147 | # Makes Cerebro Stapable in Backtests (not in live moe yet) 148 | # Disects the _runnext method into a stepable function 149 | # the reset method has to call the cerebro run method 150 | 151 | def _runnext(self, runstrats): 152 | ''' 153 | Actual implementation of run in full next mode. All objects have its 154 | ``next`` method invoke on each data arrival 155 | ''' 156 | self.runstrats_container = runstrats 157 | self._init_run() 158 | 159 | def _init_run(self): 160 | datas = sorted(self.datas, 161 | key=lambda x: (x._timeframe, x._compression)) 162 | datas1 = datas[1:] 163 | data0 = datas[0] 164 | d0ret = True 165 | rsonly = [i for i, x in enumerate(datas) 166 | if x.resampling and not x.replaying] 167 | onlyresample = len(datas) == len(rsonly) 168 | noresample = not rsonly 169 | 170 | clonecount = sum(d._clone for d in datas) 171 | ldatas = len(datas) 172 | ldatas_noclones = ldatas - clonecount 173 | dt0 = date2num(datetime.datetime.max) - 2 # default at max 174 | 175 | self.bt_state_container = { "datas" : datas, 176 | "datas1" : datas1, 177 | "data0" : data0, 178 | "d0ret" : d0ret, 179 | "rsonly" : rsonly, 180 | "onlyresample" : onlyresample, 181 | "noresample" : noresample, 182 | "ldatas_noclones" : ldatas_noclones, 183 | "dt0" : dt0, 184 | } 185 | 186 | def _step(self,runstrats,datas,datas1,data0,d0ret,rsonly, 187 | onlyresample,noresample,ldatas_noclones,dt0): 188 | 189 | # if any has live data in the buffer, no data will wait anything 190 | newqcheck = not any(d.haslivedata() for d in datas) 191 | if not newqcheck: 192 | # If no data has reached the live status or all, wait for 193 | # the next incoming data 194 | livecount = sum(d._laststatus == d.LIVE for d in datas) 195 | newqcheck = not livecount or livecount == ldatas_noclones 196 | 197 | lastret = False 198 | # Notify anything from the store even before moving datas 199 | # because datas may not move due to an error reported by the store 200 | self._storenotify() 201 | if self._event_stop: # stop if requested 202 | return True 203 | self._datanotify() 204 | if self._event_stop: # stop if requested 205 | return True 206 | 207 | # record starting time and tell feeds to discount the elapsed time 208 | # from the qcheck value 209 | drets = [] 210 | qstart = datetime.datetime.utcnow() 211 | for d in datas: 212 | qlapse = datetime.datetime.utcnow() - qstart 213 | d.do_qcheck(newqcheck, qlapse.total_seconds()) 214 | drets.append(d.next(ticks=False)) 215 | 216 | d0ret = any((dret for dret in drets)) 217 | if not d0ret and any((dret is None for dret in drets)): 218 | d0ret = None 219 | 220 | if d0ret: 221 | dts = [] 222 | for i, ret in enumerate(drets): 223 | dts.append(datas[i].datetime[0] if ret else None) 224 | 225 | # Get index to minimum datetime 226 | if onlyresample or noresample: 227 | dt0 = min((d for d in dts if d is not None)) 228 | else: 229 | dt0 = min((d for i, d in enumerate(dts) 230 | if d is not None and i not in rsonly)) 231 | 232 | dmaster = datas[dts.index(dt0)] # and timemaster 233 | self._dtmaster = dmaster.num2date(dt0) 234 | self._udtmaster = num2date(dt0) 235 | 236 | # slen = len(runstrats[0]) 237 | # Try to get something for those that didn't return 238 | for i, ret in enumerate(drets): 239 | if ret: # dts already contains a valid datetime for this i 240 | continue 241 | 242 | # try to get a data by checking with a master 243 | d = datas[i] 244 | d._check(forcedata=dmaster) # check to force output 245 | if d.next(datamaster=dmaster, ticks=False): # retry 246 | dts[i] = d.datetime[0] # good -> store 247 | # self._plotfillers2[i].append(slen) # mark as fill 248 | else: 249 | # self._plotfillers[i].append(slen) # mark as empty 250 | pass 251 | 252 | # make sure only those at dmaster level end up delivering 253 | for i, dti in enumerate(dts): 254 | if dti is not None: 255 | di = datas[i] 256 | rpi = False and di.replaying # to check behavior 257 | if dti > dt0: 258 | if not rpi: # must see all ticks ... 259 | di.rewind() # cannot deliver yet 260 | # self._plotfillers[i].append(slen) 261 | elif not di.replaying: 262 | # Replay forces tick fill, else force here 263 | di._tick_fill(force=True) 264 | 265 | # self._plotfillers2[i].append(slen) # mark as fill 266 | 267 | elif d0ret is None: 268 | # meant for things like live feeds which may not produce a bar 269 | # at the moment but need the loop to run for notifications and 270 | # getting resample and others to produce timely bars 271 | for data in datas: 272 | data._check() 273 | else: 274 | lastret = data0._last() 275 | for data in datas1: 276 | lastret += data._last(datamaster=data0) 277 | 278 | if not lastret: 279 | # Only go extra round if something was changed by "lasts" 280 | return True # return somethin signaling the end 281 | 282 | # Datas may have generated a new notification after next 283 | self._datanotify() 284 | if self._event_stop: # stop if requested 285 | return True 286 | 287 | if d0ret or lastret: # if any bar, check timers before broker 288 | self._check_timers(runstrats, dt0, cheat=True) 289 | if self.p.cheat_on_open: 290 | for strat in runstrats: 291 | strat._next_open() 292 | if self._event_stop: # stop if requested 293 | return True 294 | 295 | self._brokernotify() 296 | if self._event_stop: # stop if requested 297 | return True 298 | 299 | if d0ret or lastret: # bars produced by data or filters 300 | self._check_timers(runstrats, dt0, cheat=False) 301 | for strat in runstrats: 302 | strat._next() 303 | if self._event_stop: # stop if requested 304 | return True 305 | 306 | self._next_writers(runstrats) 307 | 308 | self.bt_state_container = { "datas" : datas, 309 | "datas1" : datas1, 310 | "data0" : data0, 311 | "d0ret" : d0ret, 312 | "rsonly" : rsonly, 313 | "onlyresample" : onlyresample, 314 | "noresample" : noresample, 315 | "ldatas_noclones" : ldatas_noclones, 316 | "dt0" : dt0, 317 | } 318 | 319 | return False 320 | 321 | def _norm(self,data): 322 | return data/np.linalg.norm(data) 323 | 324 | def _get_observations(self): 325 | 326 | if len(self.state_rows) == 1 and self.state_rows not in ["ohlc" , "ohlcv"]: 327 | line = getattr(self.bt_state_container["data0"],self.state_rows[0]) 328 | obs = np.array(list(line.get(size=self.lookback,ago=0))) 329 | if self.normalize: 330 | obs = self._norm(obs) 331 | return np.reshape(obs, (1,len(obs))) 332 | 333 | states = [] 334 | for row in self.state_rows: 335 | line = getattr(self.bt_state_container["data0"],row) 336 | 337 | obs = np.array(list(line.get(size=self.lookback,ago=0))) 338 | 339 | if self.normalize: 340 | obs = self._norm(obs) 341 | 342 | states.append(obs.tolist()) 343 | 344 | return np.array(states) 345 | -------------------------------------------------------------------------------- /backtrader_rl/strategys.py: -------------------------------------------------------------------------------- 1 | import backtrader as bt 2 | import numpy as np 3 | 4 | class BaseStrategy(bt.Strategy): 5 | 6 | def __init__(self): 7 | self.action = 1 8 | self.reward = 0 9 | self._mapping = {"buy" : 2, "sell" : 0, "hold":1} 10 | 11 | def _set_action_mapping(self,mapping): 12 | self._mapping = mapping 13 | 14 | def _setAction(self,action): 15 | self.action = action 16 | 17 | def next(self): 18 | if len(self._mapping) == 2: 19 | if self.action == self._mapping["buy"]: 20 | # if current position is sell 21 | # then we are reversing the trade 22 | if self.position.size < 0: 23 | self.buy(size = 2 * abs(self.position.size)) 24 | elif self.position.size == 0: 25 | self.buy() 26 | elif self.action == self._mapping["sell"]: 27 | # if current position is buy 28 | # then we are closing a trade 29 | if self.position.size > 0: 30 | self.sell(size = 2 * abs(self.position.size)) 31 | elif self.position.size == 0: 32 | self.sell() 33 | elif self.action == -1: 34 | pass 35 | 36 | else: 37 | 38 | if self.action == self._mapping["buy"]: 39 | # if current position is sell 40 | # then we are closing a trade 41 | if self.position.size < 0: 42 | self.close() 43 | elif self.position.size == 0: 44 | self.buy() 45 | elif self.action == self._mapping["sell"]: 46 | # if current position is buy 47 | # then we are closing a trade 48 | if self.position.size > 0: 49 | self.close() 50 | elif self.position.size == 0: 51 | self.sell() 52 | elif self.action == -1: 53 | pass 54 | 55 | def _computeReward(self): 56 | try: 57 | reward = self.computeReward() 58 | except: 59 | reward = 0 60 | self.reward = reward 61 | return self.reward 62 | 63 | class PositionBasedStrategy(BaseStrategy): 64 | 65 | def computeReward(self): 66 | if self.position.size == 0: 67 | return 0 68 | 69 | a = self.position.price 70 | b = self.datas[0].close[0] 71 | d = (b-a)/((b+a)/2) 72 | return d * 100 * (self.position.size/abs(self.position.size)) 73 | 74 | class returnBasedStrategy(BaseStrategy): 75 | 76 | def start(self): 77 | self.start_value = self.broker.get_value() 78 | 79 | def computeReward(self): 80 | 81 | return (self.broker.get_value()-self.start_value)/self.start_value * 100 82 | 83 | class SharpRatioStrategy(BaseStrategy): 84 | 85 | params = (("riskfree_rate" , 0),) 86 | 87 | def computeReward(self): 88 | trades = list(list(self._trades.copy().values())[0].values())[0] 89 | filterd_trades = list(filter(lambda x : x.isclosed, trades)) 90 | 91 | if len(filterd_trades) < 1: 92 | return 0 93 | 94 | ret = list(map(lambda x : ((x.pnl/x.price) * 100) - self.p.riskfree_rate, filterd_trades)) 95 | 96 | if np.std(ret) == 0: 97 | return 0 98 | 99 | sharp_ratio = np.mean(ret)/np.std(ret) 100 | 101 | return sharp_ratio 102 | 103 | class SortinoRatioStrategy(BaseStrategy): 104 | 105 | params = (("riskfree_rate" , 0),) 106 | 107 | def computeReward(self): 108 | trades = list(list(self._trades.copy().values())[0].values())[0] 109 | filterd_trades = list(filter(lambda x : x.isclosed, trades)) 110 | 111 | if len(filterd_trades) < 1: 112 | return 0 113 | 114 | ret = list(map(lambda x : ((x.pnl/x.price) * 100) - self.p.riskfree_rate, filterd_trades)) 115 | 116 | 117 | downside_ret = list(filter(lambda x : x < 0, ret)) 118 | 119 | if len(downside_ret) < 1 or np.std(downside_ret) == 0: 120 | return 0 121 | 122 | sharp_ratio = np.mean(ret)/np.std(downside_ret) 123 | 124 | return sharp_ratio -------------------------------------------------------------------------------- /backtrader_rl/utils.py: -------------------------------------------------------------------------------- 1 | import backtrader as bt 2 | 3 | class minPeriodIndicator(bt.Indicator): 4 | lines = ("state",) 5 | params = (('period',5),) 6 | plotinfo = dict(plot = False, 7 | subplot=False) 8 | 9 | def __init__(self) -> None: 10 | self.addminperiod(self.params.period+1) 11 | 12 | class rewardObserver(bt.Observer): 13 | alias = ("reward",) 14 | lines = ("rewards",) 15 | plotinfo = dict(plot=True, 16 | subplot=True, 17 | plotname = "Reward") 18 | 19 | def next(self): 20 | self.lines.rewards[0] = self._owner.reward 21 | 22 | class cummulativeRewardObserver(bt.Observer): 23 | alias = ("cummulativeReward",) 24 | lines = ("cummulativeRewards",) 25 | plotinfo = dict(plot=True, 26 | subplot=True, 27 | plotname = "Cumulative Reward") 28 | 29 | def next(self): 30 | if len(self.lines.cummulativeRewards) == 1: 31 | self.lines.cummulativeRewards[0] = self._owner.reward 32 | else: 33 | self.lines.cummulativeRewards[0] = self.lines.cummulativeRewards[-1] + self._owner.reward 34 | 35 | class RewardAnalyzer(bt.Analyzer): 36 | 37 | def __init__(self): 38 | self.cummulative_reward = 0 39 | self.reward_history = [] 40 | 41 | def next(self): 42 | self.reward_history.append(self.strategy.reward) 43 | self.cummulative_reward += self.strategy.reward 44 | 45 | def get_analysis(self): 46 | return dict(cummulative_reward = self.cummulative_reward, reward_history = self.reward_history) 47 | 48 | class actionObserver(bt.Observer): 49 | alias = ("action",) 50 | lines = ("actions",) 51 | 52 | plotinfo = dict(plot=True, 53 | subplot=True, 54 | plotname = "Action Space", 55 | plotyticks=(0,1,2), 56 | plothlines = (0,1,2)) 57 | 58 | def next(self): 59 | self.lines.actions[0] = self._owner.action 60 | -------------------------------------------------------------------------------- /examples/BNB_USDT_5m.csv: -------------------------------------------------------------------------------- 1 | ,timestamp,open,high,low,close,volume 2 | 0,2020-01-01 00:00:00,13.7071,13.716,13.699,13.699,1604.4 3 | 1,2020-01-01 00:05:00,13.7,13.7102,13.6988,13.7,1782.4 4 | 2,2020-01-01 00:10:00,13.701,13.7128,13.701,13.7104,2854.41 5 | 3,2020-01-01 00:15:00,13.7118,13.7198,13.7006,13.7095,1081.23 6 | 4,2020-01-01 00:20:00,13.7107,13.7123,13.7004,13.7043,891.17 7 | 5,2020-01-01 00:25:00,13.7043,13.708,13.7018,13.7018,1414.77 8 | 6,2020-01-01 00:30:00,13.7019,13.7069,13.7006,13.7069,250.03 9 | 7,2020-01-01 00:35:00,13.7008,13.7051,13.7007,13.7051,150.33 10 | 8,2020-01-01 00:40:00,13.7051,13.7197,13.7035,13.7197,2299.51 11 | 9,2020-01-01 00:45:00,13.7176,13.73,13.7147,13.73,1542.77 12 | 10,2020-01-01 00:50:00,13.7284,13.7494,13.7225,13.7425,635.75 13 | 11,2020-01-01 00:55:00,13.7469,13.7469,13.7122,13.7161,2178.86 14 | 12,2020-01-01 01:00:00,13.7159,13.7194,13.7004,13.701,6609.47 15 | 13,2020-01-01 01:05:00,13.701,13.7105,13.7,13.7079,5018.62 16 | 14,2020-01-01 01:10:00,13.7125,13.7182,13.7,13.7041,4358.32 17 | 15,2020-01-01 01:15:00,13.7051,13.7172,13.7008,13.7027,3445.14 18 | 16,2020-01-01 01:20:00,13.7024,13.7024,13.7,13.7002,8187.39 19 | 17,2020-01-01 01:25:00,13.7023,13.711,13.7,13.7048,5586.67 20 | 18,2020-01-01 01:30:00,13.7038,13.7211,13.7004,13.7053,4481.68 21 | 19,2020-01-01 01:35:00,13.7042,13.7069,13.7,13.7002,3847.14 22 | 20,2020-01-01 01:40:00,13.7002,13.7067,13.7002,13.7035,5518.74 23 | 21,2020-01-01 01:45:00,13.7043,13.7108,13.7019,13.7103,5025.31 24 | 22,2020-01-01 01:50:00,13.7094,13.7153,13.6903,13.6925,7300.01 25 | 23,2020-01-01 01:55:00,13.6917,13.7052,13.6906,13.6981,2638.2 26 | 24,2020-01-01 02:00:00,13.6981,13.7273,13.6953,13.7141,3976.52 27 | 25,2020-01-01 02:05:00,13.7141,13.7315,13.7124,13.7219,4795.39 28 | 26,2020-01-01 02:10:00,13.7204,13.7377,13.7199,13.7337,1869.75 29 | 27,2020-01-01 02:15:00,13.7318,13.7433,13.7182,13.7382,5069.92 30 | 28,2020-01-01 02:20:00,13.7443,13.7546,13.7377,13.7426,3470.39 31 | 29,2020-01-01 02:25:00,13.7438,13.8,13.7428,13.7854,9327.15 32 | 30,2020-01-01 02:30:00,13.7902,13.8,13.7746,13.796,3117.83 33 | 31,2020-01-01 02:35:00,13.7979,13.798,13.784,13.7897,1186.74 34 | 32,2020-01-01 02:40:00,13.7898,13.79,13.7863,13.7878,6068.08 35 | 33,2020-01-01 02:45:00,13.7878,13.79,13.7795,13.7892,3807.32 36 | 34,2020-01-01 02:50:00,13.7885,13.8,13.7885,13.7991,3757.03 37 | 35,2020-01-01 02:55:00,13.7952,13.7998,13.7904,13.7995,7345.13 38 | 36,2020-01-01 03:00:00,13.7984,13.8,13.7906,13.7958,9305.23 39 | 37,2020-01-01 03:05:00,13.7969,13.7969,13.7906,13.7947,7104.65 40 | 38,2020-01-01 03:10:00,13.7947,13.8261,13.7924,13.8028,6464.71 41 | 39,2020-01-01 03:15:00,13.8028,13.8261,13.7992,13.825,7043.85 42 | 40,2020-01-01 03:20:00,13.8234,13.8243,13.8037,13.8216,3405.91 43 | 41,2020-01-01 03:25:00,13.8199,13.8287,13.7993,13.8123,7692.33 44 | 42,2020-01-01 03:30:00,13.811,13.8376,13.811,13.8216,3728.95 45 | 43,2020-01-01 03:35:00,13.8145,13.8293,13.8058,13.8237,6288.38 46 | 44,2020-01-01 03:40:00,13.8255,13.8297,13.8205,13.8287,2941.97 47 | 45,2020-01-01 03:45:00,13.8289,13.8422,13.8241,13.8422,5529.1 48 | 46,2020-01-01 03:50:00,13.8422,13.8449,13.8391,13.8401,3397.21 49 | 47,2020-01-01 03:55:00,13.8401,13.8488,13.839,13.8478,12298.2 50 | 48,2020-01-01 04:00:00,13.8461,13.85,13.8087,13.8203,11904.48 51 | 49,2020-01-01 04:05:00,13.8173,13.8205,13.7881,13.7909,4023.65 52 | 50,2020-01-01 04:10:00,13.7887,13.8002,13.7855,13.7922,1869.77 53 | 51,2020-01-01 04:15:00,13.791,13.8093,13.7895,13.8013,1882.18 54 | 52,2020-01-01 04:20:00,13.8036,13.8235,13.8036,13.8202,775.46 55 | 53,2020-01-01 04:25:00,13.8201,13.822,13.817,13.8209,497.7 56 | 54,2020-01-01 04:30:00,13.8199,13.8364,13.8199,13.835,3523.31 57 | 55,2020-01-01 04:35:00,13.8349,13.8417,13.818,13.8257,2231.97 58 | 56,2020-01-01 04:40:00,13.825,13.8295,13.8089,13.8099,3232.91 59 | 57,2020-01-01 04:45:00,13.8095,13.8173,13.8073,13.8173,1674.06 60 | 58,2020-01-01 04:50:00,13.8173,13.8173,13.8092,13.8118,1916.62 61 | 59,2020-01-01 04:55:00,13.8173,13.8205,13.8101,13.817,2463.86 62 | 60,2020-01-01 05:00:00,13.8157,13.8203,13.81,13.8164,2291.47 63 | 61,2020-01-01 05:05:00,13.8194,13.8203,13.811,13.8193,2711.54 64 | 62,2020-01-01 05:10:00,13.8193,13.8193,13.8003,13.8009,3726.41 65 | 63,2020-01-01 05:15:00,13.8009,13.8079,13.8007,13.8073,3462.59 66 | 64,2020-01-01 05:20:00,13.8073,13.8153,13.8007,13.8053,3220.16 67 | 65,2020-01-01 05:25:00,13.8041,13.812,13.8007,13.8067,3821.46 68 | 66,2020-01-01 05:30:00,13.808,13.8262,13.8059,13.8206,2783.13 69 | 67,2020-01-01 05:35:00,13.8206,13.823,13.8031,13.8144,2762.5 70 | 68,2020-01-01 05:40:00,13.8154,13.8154,13.8048,13.8078,2097.8 71 | 69,2020-01-01 05:45:00,13.807,13.8142,13.805,13.8068,3109.56 72 | 70,2020-01-01 05:50:00,13.8069,13.8072,13.8,13.8,1643.67 73 | 71,2020-01-01 05:55:00,13.8008,13.8008,13.7801,13.7922,2779.26 74 | 72,2020-01-01 06:00:00,13.7968,13.8009,13.7889,13.7999,2836.29 75 | 73,2020-01-01 06:05:00,13.7999,13.8127,13.7985,13.8117,4994.48 76 | 74,2020-01-01 06:10:00,13.8144,13.8144,13.8051,13.806,2388.13 77 | 75,2020-01-01 06:15:00,13.8059,13.8111,13.7997,13.7997,3910.12 78 | 76,2020-01-01 06:20:00,13.7997,13.8069,13.7987,13.799,2313.43 79 | 77,2020-01-01 06:25:00,13.8,13.8,13.7985,13.799,4883.98 80 | 78,2020-01-01 06:30:00,13.799,13.8,13.7986,13.7997,5763.29 81 | 79,2020-01-01 06:35:00,13.7997,13.812,13.7989,13.8078,2398.37 82 | 80,2020-01-01 06:40:00,13.81,13.81,13.804,13.8089,3612.65 83 | 81,2020-01-01 06:45:00,13.804,13.81,13.8039,13.809,4088.02 84 | 82,2020-01-01 06:50:00,13.809,13.8099,13.7991,13.8047,1842.58 85 | 83,2020-01-01 06:55:00,13.8046,13.8089,13.7985,13.8007,3843.69 86 | 84,2020-01-01 07:00:00,13.8007,13.8047,13.7988,13.8047,2753.4 87 | 85,2020-01-01 07:05:00,13.8026,13.8097,13.801,13.806,3365.35 88 | 86,2020-01-01 07:10:00,13.8088,13.8154,13.8078,13.8106,1912.57 89 | 87,2020-01-01 07:15:00,13.812,13.826,13.8119,13.8183,2155.89 90 | 88,2020-01-01 07:20:00,13.8195,13.829,13.8195,13.8279,2864.33 91 | 89,2020-01-01 07:25:00,13.8279,13.829,13.8191,13.828,2419.62 92 | 90,2020-01-01 07:30:00,13.828,13.8329,13.8206,13.8222,1843.24 93 | 91,2020-01-01 07:35:00,13.8224,13.8317,13.8214,13.8295,3123.57 94 | 92,2020-01-01 07:40:00,13.8317,13.8501,13.8303,13.842,1775.63 95 | 93,2020-01-01 07:45:00,13.8386,13.8443,13.827,13.8308,1041.86 96 | 94,2020-01-01 07:50:00,13.8319,13.8351,13.8204,13.8214,837.87 97 | 95,2020-01-01 07:55:00,13.8202,13.8247,13.815,13.8203,1184.4 98 | 96,2020-01-01 08:00:00,13.8217,13.8298,13.8151,13.8222,1617.99 99 | 97,2020-01-01 08:05:00,13.822,13.8222,13.821,13.8213,1632.7 100 | 98,2020-01-01 08:10:00,13.8213,13.8268,13.8147,13.8247,1757.76 101 | 99,2020-01-01 08:15:00,13.8258,13.8327,13.8171,13.8309,1547.41 102 | 100,2020-01-01 08:20:00,13.8309,13.8309,13.8208,13.8296,1199.69 103 | 101,2020-01-01 08:25:00,13.827,13.8321,13.8217,13.8235,1898.11 104 | 102,2020-01-01 08:30:00,13.8235,13.8241,13.8055,13.8078,1954.98 105 | 103,2020-01-01 08:35:00,13.8081,13.8172,13.8055,13.8055,1493.8 106 | 104,2020-01-01 08:40:00,13.8056,13.8056,13.7663,13.7711,7263.84 107 | 105,2020-01-01 08:45:00,13.7669,13.7686,13.745,13.7646,4820.76 108 | 106,2020-01-01 08:50:00,13.7623,13.7623,13.7428,13.75,2595.72 109 | 107,2020-01-01 08:55:00,13.749,13.7621,13.749,13.7608,2575.95 110 | 108,2020-01-01 09:00:00,13.7618,13.7621,13.7336,13.7352,3293.0 111 | 109,2020-01-01 09:05:00,13.7311,13.75,13.7305,13.7418,2545.01 112 | 110,2020-01-01 09:10:00,13.7406,13.7424,13.7293,13.7364,2406.82 113 | 111,2020-01-01 09:15:00,13.7325,13.7362,13.7008,13.7112,3094.87 114 | 112,2020-01-01 09:20:00,13.7123,13.7199,13.7092,13.7153,1818.92 115 | 113,2020-01-01 09:25:00,13.7153,13.7345,13.7076,13.7271,5321.54 116 | 114,2020-01-01 09:30:00,13.7228,13.724,13.6994,13.7112,2839.53 117 | 115,2020-01-01 09:35:00,13.7145,13.7183,13.7027,13.7104,3029.35 118 | 116,2020-01-01 09:40:00,13.717,13.7256,13.7017,13.7098,3753.17 119 | 117,2020-01-01 09:45:00,13.701,13.7163,13.699,13.7163,2358.3 120 | 118,2020-01-01 09:50:00,13.7105,13.7172,13.7067,13.7156,1892.06 121 | 119,2020-01-01 09:55:00,13.7157,13.73,13.7157,13.7299,3067.63 122 | 120,2020-01-01 10:00:00,13.7289,13.7375,13.7176,13.7239,2585.78 123 | 121,2020-01-01 10:05:00,13.7239,13.7361,13.7194,13.7256,1436.78 124 | 122,2020-01-01 10:10:00,13.7242,13.7269,13.7194,13.7224,2413.05 125 | 123,2020-01-01 10:15:00,13.7223,13.7233,13.7157,13.718,3386.96 126 | 124,2020-01-01 10:20:00,13.719,13.7193,13.712,13.7164,4237.19 127 | 125,2020-01-01 10:25:00,13.7184,13.7184,13.696,13.6998,3006.82 128 | 126,2020-01-01 10:30:00,13.6997,13.7235,13.6997,13.7206,2268.48 129 | 127,2020-01-01 10:35:00,13.7253,13.73,13.7194,13.721,1989.42 130 | 128,2020-01-01 10:40:00,13.7251,13.7269,13.7199,13.7256,2396.75 131 | 129,2020-01-01 10:45:00,13.7256,13.7256,13.7191,13.7239,3872.11 132 | 130,2020-01-01 10:50:00,13.7228,13.724,13.7,13.7009,1865.97 133 | 131,2020-01-01 10:55:00,13.6998,13.7053,13.6981,13.7019,1427.06 134 | 132,2020-01-01 11:00:00,13.7013,13.7019,13.6813,13.692,3227.45 135 | 133,2020-01-01 11:05:00,13.6919,13.7142,13.6919,13.7142,2860.93 136 | 134,2020-01-01 11:10:00,13.7145,13.7269,13.7089,13.7234,2405.78 137 | 135,2020-01-01 11:15:00,13.7222,13.728,13.7203,13.7246,2714.44 138 | 136,2020-01-01 11:20:00,13.7228,13.7239,13.7135,13.716,2526.5 139 | 137,2020-01-01 11:25:00,13.716,13.719,13.7118,13.719,2485.18 140 | 138,2020-01-01 11:30:00,13.719,13.7241,13.7081,13.7089,2579.7 141 | 139,2020-01-01 11:35:00,13.7084,13.7187,13.7084,13.7184,3848.13 142 | 140,2020-01-01 11:40:00,13.7173,13.725,13.7096,13.725,2748.72 143 | 141,2020-01-01 11:45:00,13.7228,13.73,13.7227,13.729,4646.03 144 | 142,2020-01-01 11:50:00,13.7243,13.7301,13.7231,13.7288,3038.62 145 | 143,2020-01-01 11:55:00,13.7286,13.7477,13.7275,13.7434,4413.62 146 | 144,2020-01-01 12:00:00,13.7453,13.7738,13.7372,13.7672,6183.76 147 | 145,2020-01-01 12:05:00,13.7663,13.785,13.7656,13.7696,4377.15 148 | 146,2020-01-01 12:10:00,13.7696,13.7792,13.7482,13.7584,6881.18 149 | 147,2020-01-01 12:15:00,13.7592,13.7747,13.7552,13.7635,3914.31 150 | 148,2020-01-01 12:20:00,13.7648,13.7894,13.7633,13.7894,5093.36 151 | 149,2020-01-01 12:25:00,13.7894,13.8125,13.7797,13.7931,8807.57 152 | 150,2020-01-01 12:30:00,13.7931,13.8052,13.771,13.7773,5541.26 153 | 151,2020-01-01 12:35:00,13.7766,13.7785,13.7604,13.7647,3926.3 154 | 152,2020-01-01 12:40:00,13.7636,13.7807,13.763,13.7788,3457.52 155 | 153,2020-01-01 12:45:00,13.7788,13.7954,13.7622,13.7622,5826.88 156 | 154,2020-01-01 12:50:00,13.7605,13.7663,13.7127,13.7148,10468.66 157 | 155,2020-01-01 12:55:00,13.7148,13.7246,13.7079,13.719,2970.8 158 | 156,2020-01-01 13:00:00,13.719,13.7696,13.7168,13.7683,5225.02 159 | 157,2020-01-01 13:05:00,13.7637,13.7684,13.7402,13.75,5424.75 160 | 158,2020-01-01 13:10:00,13.7527,13.7563,13.7404,13.7436,1827.4 161 | 159,2020-01-01 13:15:00,13.7446,13.7647,13.7425,13.7647,4307.01 162 | 160,2020-01-01 13:20:00,13.7657,13.7999,13.763,13.7999,2860.17 163 | 161,2020-01-01 13:25:00,13.7998,13.7998,13.7635,13.7667,5171.8 164 | 162,2020-01-01 13:30:00,13.7669,13.7816,13.7636,13.7774,3690.86 165 | 163,2020-01-01 13:35:00,13.7865,13.7938,13.7749,13.7817,4421.89 166 | 164,2020-01-01 13:40:00,13.7847,13.7873,13.7713,13.7795,2562.41 167 | 165,2020-01-01 13:45:00,13.771,13.7981,13.77,13.7908,3040.26 168 | 166,2020-01-01 13:50:00,13.7848,13.8,13.7758,13.7973,4962.51 169 | 167,2020-01-01 13:55:00,13.7981,13.7999,13.7973,13.7998,3978.15 170 | 168,2020-01-01 14:00:00,13.7985,13.8174,13.7974,13.7988,6719.99 171 | 169,2020-01-01 14:05:00,13.7986,13.8054,13.7918,13.804,3911.31 172 | 170,2020-01-01 14:10:00,13.8041,13.8241,13.8041,13.8208,3675.73 173 | 171,2020-01-01 14:15:00,13.8208,13.8211,13.7983,13.7989,3383.81 174 | 172,2020-01-01 14:20:00,13.7971,13.8137,13.797,13.8137,2685.2 175 | 173,2020-01-01 14:25:00,13.8115,13.8212,13.8103,13.8132,1521.82 176 | 174,2020-01-01 14:30:00,13.8132,13.8262,13.8111,13.8262,1898.72 177 | 175,2020-01-01 14:35:00,13.8262,13.8449,13.8246,13.8262,1764.04 178 | 176,2020-01-01 14:40:00,13.8206,13.8421,13.8206,13.8335,2883.94 179 | 177,2020-01-01 14:45:00,13.8347,13.8347,13.814,13.8277,1928.0 180 | 178,2020-01-01 14:50:00,13.8287,13.829,13.8179,13.8245,1469.07 181 | 179,2020-01-01 14:55:00,13.824,13.8256,13.8112,13.812,770.02 182 | 180,2020-01-01 15:00:00,13.8199,13.8201,13.7921,13.8019,2016.41 183 | 181,2020-01-01 15:05:00,13.8007,13.8007,13.7785,13.7948,2069.8 184 | 182,2020-01-01 15:10:00,13.7948,13.7949,13.7826,13.7906,1524.39 185 | 183,2020-01-01 15:15:00,13.7938,13.8101,13.7919,13.7988,3074.97 186 | 184,2020-01-01 15:20:00,13.809,13.8264,13.8047,13.8164,3698.75 187 | 185,2020-01-01 15:25:00,13.8166,13.8271,13.8102,13.8252,2748.47 188 | 186,2020-01-01 15:30:00,13.8261,13.8494,13.8261,13.8469,3900.09 189 | 187,2020-01-01 15:35:00,13.8478,13.8692,13.8382,13.8424,4191.33 190 | 188,2020-01-01 15:40:00,13.8464,13.85,13.84,13.8401,2225.91 191 | 189,2020-01-01 15:45:00,13.8411,13.8442,13.8335,13.8345,1839.94 192 | 190,2020-01-01 15:50:00,13.8335,13.8359,13.8161,13.8161,3498.32 193 | 191,2020-01-01 15:55:00,13.8164,13.8316,13.814,13.8274,3123.95 194 | 192,2020-01-01 16:00:00,13.8315,13.8397,13.8254,13.83,2281.62 195 | 193,2020-01-01 16:05:00,13.83,13.8344,13.8193,13.8251,2205.85 196 | 194,2020-01-01 16:10:00,13.8239,13.8259,13.8081,13.8149,2298.45 197 | 195,2020-01-01 16:15:00,13.8149,13.818,13.8057,13.8057,1780.6 198 | 196,2020-01-01 16:20:00,13.809,13.8127,13.7786,13.7856,3875.51 199 | 197,2020-01-01 16:25:00,13.7856,13.8151,13.7804,13.813,3672.58 200 | 198,2020-01-01 16:30:00,13.8136,13.8247,13.8107,13.8124,2571.2 201 | 199,2020-01-01 16:35:00,13.815,13.86,13.8033,13.8465,6245.08 202 | 200,2020-01-01 16:40:00,13.8473,13.856,13.8226,13.8239,2726.24 203 | 201,2020-01-01 16:45:00,13.8235,13.8345,13.809,13.8181,3026.68 204 | 202,2020-01-01 16:50:00,13.8118,13.8236,13.8096,13.8134,3393.09 205 | 203,2020-01-01 16:55:00,13.8134,13.8223,13.8126,13.8223,2195.89 206 | 204,2020-01-01 17:00:00,13.8223,13.8301,13.813,13.8174,3000.1 207 | 205,2020-01-01 17:05:00,13.8294,13.8298,13.8095,13.8095,3073.16 208 | 206,2020-01-01 17:10:00,13.8099,13.8241,13.8095,13.8241,838.28 209 | 207,2020-01-01 17:15:00,13.8254,13.8254,13.8003,13.8038,2436.43 210 | 208,2020-01-01 17:20:00,13.8037,13.8146,13.7861,13.801,2653.29 211 | 209,2020-01-01 17:25:00,13.7976,13.8132,13.7961,13.8123,1979.39 212 | 210,2020-01-01 17:30:00,13.8111,13.812,13.8004,13.8091,1625.93 213 | 211,2020-01-01 17:35:00,13.8114,13.8211,13.8048,13.8199,2489.85 214 | 212,2020-01-01 17:40:00,13.8104,13.8262,13.8104,13.8262,2471.43 215 | 213,2020-01-01 17:45:00,13.8262,13.8262,13.8082,13.8261,7120.01 216 | 214,2020-01-01 17:50:00,13.821,13.8475,13.8181,13.8458,2852.21 217 | 215,2020-01-01 17:55:00,13.8462,13.8661,13.8394,13.8522,3083.9 218 | -------------------------------------------------------------------------------- /examples/downloader.py: -------------------------------------------------------------------------------- 1 | import ccxt 2 | import pandas as pd 3 | import numpy as np 4 | import datetime 5 | import time 6 | 7 | def toDatetime(value): 8 | return datetime.datetime.fromtimestamp(int(value/1000)) 9 | 10 | binance = ccxt.binance() 11 | 12 | pair = "BTC/USDT" 13 | timeframe = "1h" 14 | timedelta = datetime.timedelta(hours = 1) 15 | chunk_size = 1000 # max is 1000 16 | normalize = False 17 | 18 | p = pair.split("/") 19 | csv_name = f"examples/{p[0]}_{p[1]}_{timeframe}.csv" 20 | 21 | save_interval = 1000 22 | 23 | start = datetime.datetime(2021,1,1) 24 | end = datetime.datetime(2022,1,1) 25 | 26 | steps = int((end-start)/(timedelta)) 27 | num = int(np.ceil(steps/chunk_size)) if end else 10 28 | 29 | t = int(start.timestamp() * 1000) 30 | df = pd.DataFrame(columns = ["timestamp","open","high","low","close","volume"]) 31 | 32 | for i in range(num): 33 | candles = np.array(binance.fetch_ohlcv( pair, 34 | timeframe, 35 | limit = chunk_size + 1 if i != num -1 else steps - (num-1) * chunk_size, 36 | since=t)) 37 | 38 | if normalize: 39 | for n in range(1,6): 40 | candles[:,n] = (candles[:,n] - candles[:,n].min())/ (candles[:,n].max() - candles[:,n].min()) 41 | 42 | temp = pd.DataFrame(candles, columns = ["timestamp","open","high","low","close","volume"]) 43 | t = int(temp.tail(1).timestamp) + timedelta.seconds * 1000 44 | 45 | temp.timestamp = list(map(toDatetime,candles[:,0])) 46 | 47 | df = pd.concat([df, temp], ignore_index=True) 48 | 49 | print(f"{i*chunk_size}/{steps}") 50 | df.to_csv(csv_name) 51 | 52 | time.sleep(0.5) 53 | 54 | # TODO clean up and interpolate when time index is missing 55 | 56 | print(f"{steps}/{steps}") 57 | 58 | print("done") 59 | print("Dataframe length {}".format(len(df))) 60 | 61 | df.to_csv(csv_name) -------------------------------------------------------------------------------- /examples/gym_env_example.py: -------------------------------------------------------------------------------- 1 | from __future__ import (absolute_import, division, print_function, 2 | unicode_literals) 3 | 4 | import pandas as pd 5 | 6 | from random import randint 7 | 8 | import backtrader as bt 9 | from backtrader_rl.strategys import returnBasedStrategy 10 | from backtrader_rl.utils import actionObserver,rewardObserver,cummulativeRewardObserver 11 | from backtrader_rl.engines import BTEngine 12 | from backtrader_rl.adapters.gymAdapter import gymAdapter 13 | 14 | # ================ 15 | # Defining constants 16 | # ================ 17 | 18 | # the length of data points available to the agent at each step 19 | LOOKBACK = 40 20 | 21 | 22 | # ================ 23 | # Preparing test data 24 | # ================ 25 | 26 | from pathlib import Path 27 | root = Path().absolute() 28 | file = "examples\BNB_USDT_5m.csv" 29 | df = pd.read_csv(Path(root,file),index_col=0) 30 | df["timestamp"] = pd.to_datetime(df["timestamp"]) 31 | df = df.set_index("timestamp",drop=True) 32 | data = bt.feeds.PandasData(dataname=df) 33 | 34 | engine = BTEngine(lookback = LOOKBACK) 35 | engine.broker.set_cash(100) 36 | engine.adddata(data) 37 | 38 | # ================ 39 | # Defining the Strategy 40 | # ================ 41 | 42 | # the strategy defines the reward schema 43 | # the position based strategy uses the position PNL as a reward at each step 44 | engine.addstrategy(returnBasedStrategy) 45 | 46 | # observeres allow us to peak into the actions taken by the agent over the episode 47 | engine.addobserver(actionObserver) 48 | engine.addobserver(rewardObserver) 49 | engine.addobserver(cummulativeRewardObserver) 50 | 51 | 52 | # default backtrader observers also work just fine 53 | engine.addobserver(bt.observers.BuySell) 54 | engine.addobserver(bt.observers.Broker) 55 | engine.addobserver(bt.observers.Trades) 56 | 57 | 58 | # ================ 59 | # use the openai gym environment 60 | # ================ 61 | 62 | env = gymAdapter(engine) 63 | 64 | env.reset() 65 | terminated = False 66 | 67 | rewards = [] 68 | 69 | while not terminated: 70 | observation, reward, terminated, truncated, info = env.step(randint(0,2)) 71 | 72 | env.close() 73 | env.render() -------------------------------------------------------------------------------- /examples/raylib_example.py: -------------------------------------------------------------------------------- 1 | from __future__ import (absolute_import, division, print_function, 2 | unicode_literals) 3 | 4 | import pandas as pd 5 | 6 | import backtrader as bt 7 | from backtrader_rl.strategys import PositionBasedStrategy as strat 8 | from backtrader_rl.utils import actionObserver,rewardObserver,cummulativeRewardObserver 9 | from backtrader_rl.engines import BTEngine 10 | from backtrader_rl.adapters.tensorforceAdapter import tensorforceAdapter 11 | 12 | from tensorforce.agents import Agent 13 | from tensorforce.environments import Environment 14 | from tensorforce.execution import Runner 15 | 16 | import matplotlib.pyplot as plt 17 | import numpy as np 18 | # ================ 19 | # Defining constants 20 | # ================ 21 | 22 | # the length of data points available to the agent at each step 23 | LOOKBACK = 40 24 | DATA_LENGTH = 10000 25 | 26 | # ================ 27 | # Preparing test data 28 | # ================ 29 | 30 | from pathlib import Path 31 | root = Path().absolute() 32 | file = "examples\BTC_USDT_5m.csv" 33 | df = pd.read_csv(Path(root,file),index_col=0) 34 | df["timestamp"] = pd.to_datetime(df["timestamp"]) 35 | df = df.set_index("timestamp",drop=True) 36 | 37 | print(df.isnull().values.any()) -------------------------------------------------------------------------------- /examples/tensorforce_example.py: -------------------------------------------------------------------------------- 1 | from __future__ import (absolute_import, division, print_function, 2 | unicode_literals) 3 | 4 | import pandas as pd 5 | 6 | import backtrader as bt 7 | from backtrader_rl.strategys import BaseStrategy 8 | from backtrader_rl.utils import actionObserver,rewardObserver,cummulativeRewardObserver 9 | from backtrader_rl.engines import BTEngine 10 | from backtrader_rl.adapters.tensorforceAdapter import tensorforceAdapter 11 | 12 | from tensorforce.agents import Agent 13 | from tensorforce.environments import Environment 14 | from tensorforce.execution import Runner 15 | 16 | import matplotlib.pyplot as plt 17 | import numpy as np 18 | 19 | # ================ 20 | # Reward Shema 21 | # ================ 22 | 23 | class RewardSchema(BaseStrategy): 24 | 25 | lastRef = -1 26 | total = [] 27 | down = [] 28 | lastSortino = 0 29 | lastNonZeroChange = 0 30 | steps_from_change = 0 31 | useLastingReward = True 32 | 33 | def claclSortino(self): 34 | 35 | trades = list(list(self._trades.copy().values())[0].values())[0] 36 | filterd_trades = list(filter(lambda x : x.isclosed, trades)) 37 | 38 | if len(filterd_trades) < 1: 39 | return 0 40 | 41 | lastTrade = filterd_trades[-1] 42 | 43 | if self.lastRef != lastTrade.ref: 44 | self.lastRef = lastTrade.ref 45 | ret = (lastTrade.pnlcomm/lastTrade.price)*100 46 | if ret <= 0: 47 | self.down.append(ret) 48 | self.total.append(ret) 49 | 50 | m = np.mean(self.total) 51 | 52 | std = np.std(self.down) 53 | 54 | if std == 0 or type(std) == np.nan: 55 | return 0 56 | 57 | 58 | sr = np.sqrt(len(filterd_trades)) 59 | 60 | return (m*sr)/std 61 | 62 | # cal sortiono ratio 63 | def computeReward(self): 64 | 65 | sortino = self.claclSortino() 66 | 67 | change = sortino - self.lastSortino 68 | change = change if not np.isnan(change) else 0 69 | 70 | if change != 0: 71 | self.lastNonZeroChange = change 72 | self.steps_from_change = 0 73 | else: 74 | self.steps_from_change += 1 75 | 76 | self.lastSortino = sortino 77 | 78 | if self.useLastingReward: 79 | return (self.lastNonZeroChange / (self.steps_from_change + 1)) 80 | 81 | return change 82 | 83 | # ================ 84 | # Defining constants 85 | # ================ 86 | 87 | # the length of data points available to the agent at each step 88 | LOOKBACK = 15 89 | DATA_LENGTH = 10000 90 | 91 | # ================ 92 | # Preparing test data 93 | # ================ 94 | 95 | from pathlib import Path 96 | root = Path().absolute() 97 | file_name = "examples\BTC_USDT_5m.csv" 98 | df = pd.read_csv(Path(root,file_name),index_col=0) 99 | df["timestamp"] = pd.to_datetime(df["timestamp"]) 100 | df = df.set_index("timestamp",drop=True) 101 | 102 | # df = df.iloc[500:500+DATA_LENGTH] 103 | # df = df.reset_index() 104 | 105 | 106 | data = bt.feeds.PandasData(dataname=df) 107 | 108 | engine = BTEngine( lookback = LOOKBACK, 109 | state_rows=("ohlcv",), 110 | normalize = True, 111 | canhold = True) 112 | 113 | engine.broker.set_cash(100) 114 | engine.adddata(data) 115 | 116 | # ================ 117 | # Defining the Strategy 118 | # ================ 119 | 120 | # the strategy defines the reward schema 121 | # the position based strategy uses the position PNL as a reward at each step 122 | engine.addstrategy(RewardSchema) 123 | 124 | # observeres allow us to peak into the actions taken by the agent over the episode 125 | engine.addobserver(actionObserver) 126 | engine.addobserver(rewardObserver) 127 | engine.addobserver(cummulativeRewardObserver) 128 | 129 | 130 | # default backtrader observers also work just fine 131 | engine.addobserver(bt.observers.BuySell) 132 | engine.addobserver(bt.observers.Value) 133 | engine.addobserver(bt.observers.Trades) 134 | 135 | # 136 | 137 | environment = Environment.create( 138 | environment=tensorforceAdapter(engine), max_episode_timesteps=len(df)-LOOKBACK-1 139 | ) 140 | 141 | agent = Agent.create( 142 | agent = "ppo" , environment = environment, batch_size = 2 143 | ) 144 | 145 | import time 146 | 147 | times = [] 148 | for episode in range(5): 149 | # Episode using act and observe 150 | start_time = time.time() 151 | num_steps = 0 152 | states = environment.reset() 153 | terminal = False 154 | sum_rewards = 0.0 155 | num_updates = 0 156 | 157 | while not terminal: 158 | actions = agent.act(states=states) 159 | states, terminal, reward = environment.execute(actions=actions) 160 | num_updates += agent.observe(terminal=terminal, reward=reward) 161 | sum_rewards += reward 162 | num_steps += 1 163 | 164 | print('Episode {}: return= {} updates= {}'.format(episode, sum_rewards, num_updates)) 165 | 166 | end_time = time.time() 167 | times.append(end_time-start_time) 168 | 169 | print("total time[s]: ", sum(times)) 170 | print("time per episode", np.mean(times)) 171 | print("total steps per episode: ", num_steps) 172 | print("seconds per step: ", np.mean(times)/num_steps) 173 | 174 | # Evaluate for 100 episodes 175 | sum_rewards = 0.0 176 | num = 1 177 | for _ in range(num): 178 | states = environment.reset() 179 | internals = agent.initial_internals() 180 | terminal = False 181 | while not terminal: 182 | actions, internals = agent.act( 183 | states=states, internals=internals, independent=True, deterministic=False 184 | ) 185 | states, terminal, reward = environment.execute(actions=actions) 186 | sum_rewards += reward 187 | 188 | print('Mean evaluation return:', sum_rewards / num) 189 | 190 | environment.plot() 191 | 192 | # Close agent and environment 193 | agent.close() 194 | environment.close() -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name='backtrader_rl', 5 | version='0.2', 6 | description='Reinforcement Environment for Finacial trading based on the Backtrader infrastructure', 7 | author='Max Lödige', 8 | # author_email='foomail@foo.com', 9 | packages = ["backtrader_rl","backtrader_rl.adapters"], # would be the same as name 10 | install_requires=['backtrader', 'gym', 'tensorforce', 'ray[rllib]'], #external packages acting as dependencies 11 | ) 12 | --------------------------------------------------------------------------------