Specifically, how do I set 'volume', 'volume_step', 'volume_min' and 'volume_max' without creating a child of the Order class? I need to be able to set these values potentially for each order.

hello @AminHP , There is a bug in the check volume function inside the simulator, you write :

 def _check_volume(self, symbol: str, volume: float) -> None:
        symbol_info = self.symbols_info[symbol]
        if not (symbol_info.volume_min <= volume <= symbol_info.volume_max):
            raise ValueError(
                f"'volume' must be in range [{symbol_info.volume_min}, {symbol_info.volume_max}]"
            )
        if not round(volume / symbol_info.volume_step, 6).is_integer():
            raise ValueError(f"'volume' must be a multiple of {symbol_info.volume_step}")

you are rounding the volume to 6 decimals and expecting an integer as a result, this can only be true if the passed volume was an integer, your error message says mutiple of 0.01 (volume step), this also contradicts with your voume check function inside env._get_modified_volume where you expect volume to be mutiple of 0.01 as well.

Hello @AminHP , when running one of your examples (Create an environment with custom parameters), i get the follwoing error: File "", line 1, in File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\spawn.py", line 125, in _main prepare(preparation_data) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "C:\Program Files\Python39\lib\runpy.py", line 268, in run_path return _run_module_code(code, init_globals, run_name, File "C:\Program Files\Python39\lib\runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, File "C:\Program Files\Python39\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "c:\Users\Ali.Khankan\Desktop\gym-mtsim\ali_test.py", line 16, in env = MtEnv( File "c:\Users\Ali.Khankan\Desktop\gym-mtsim\gym_mtsim\envs\mt_env.py", line 63, in init self.multiprocessing_pool = Pool(multiprocessing_processes) if multiprocessing_processes else None File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\pathos\multiprocessing.py", line 111, in init self._serve() File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\pathos\multiprocessing.py", line 123, in _serve _pool = Pool(nodes) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\pool.py", line 212, in init self._repopulate_pool() File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\pool.py", line 303, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\pool.py", line 326, in _repopulate_pool_static w.start() File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\process.py", line 121, in start self._popen = self._Popen(self) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\context.py", line 327, in _Popen return Popen(process_obj) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\popen_spawn_win32.py", line 45, in init prep_data = spawn.get_preparation_data(process_obj._name) File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "C:\Users\Ali.Khankan\AppData\Roaming\Python\Python39\site-packages\multiprocess\spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase.

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

setting multiprocessing_processes = 0 solved the problem

Hello @AminHP , I am struggeling to understand your action space, you say quote: " The action space is a 1D vector of size count(trading_symbols) * (symbol_max_orders + 2). For each symbol, two types of actions can be performed, closing previous orders and placing a new order. The latter is controlled by the first symbol_max_orders elements and the former is controlled by the last two elements."

you also write:

self.action_space = spaces.Box(
            low=-np.inf, high=np.inf,
            shape=(len(self.trading_symbols) * (self.symbol_max_orders + 2),)
        )  #

why do you +2 to the symbole_max_orders? what are the last 2 elements you are referring to?

I have tried many time and method for this. I have writen the code below, could you please check for me whether I have done something wrong. In this case I left only this 2 informations for the experiment

model_balance = Sequential() model_balance.add(Flatten(input_shape=(1,1,1), name='balance')) model_balance_input = Input(shape=(1,1), name='balance') model_balance_encoded = model_balance(model_balance_input)

model_equity = Sequential() model_equity.add(Flatten(input_shape=(1,1,1), name='equity')) model_equity_input = Input(shape=(1,1), name='equity') model_equity_encoded = model_Equity(model_equity_input)

con = concatenate([model_Balance_encoded, model_Equity_encoded])

dense = Dense(1024, activation='relu')(con) dense = Dense(1024, activation='relu')(dense) output = Dense(actions, activation='softmax')(dense)

model = Model(inputs=[model_Balance_input, model_Equity_input], outputs=output)

memory = SequentialMemory(limit=2000000, window_length=1) policy = LinearAnnealedPolicy(EpsGreedyQPolicy()) dqn = DQNAgent(model=model, policy=policy, nb_actions=actions, memory=memory)

dqn.processor = MultiInputProcessor(2)

dqn.compile( optimizer = Adam(learning_rate=1e-4), metrics = ['mse'] #'accuracy', 'mae')

dqn.fit(env, nb_steps=1000, verbose=1, visualize=False,)

==================================================================== Then, this problem occured, and I could not find the error.

please tell me the solution. thanks in advance

for example , my action space has 7 discrete actions, and they correspond to 7 different positions as follows: heavy long postion (80% long position size) mid long postion (50% long position size) light long postion (30% long position size) not holding any symbols, meaning position size is 0 light short postion (30% short position size) mid short postion (50% short position size) heavy short postion (80% short position size)

In order to achieve this, what should I do？

Hello, Thank you so much for the simple and wonderful library. can you please explain what is this fucntion doing? why do we need to get the unit ratio to calculate order profit?

    def _get_unit_ratio(self, symbol: str, time: datetime) -> float:
        symbol_info = self.symbols_info[symbol]
        if self.unit == symbol_info.currency_profit:
            return 1.

        if self.unit == symbol_info.currency_margin:
            return 1 / self.price_at(symbol, time)['Close']

        currency = symbol_info.currency_profit
        unit_symbol_info = self._get_unit_symbol_info(currency)
        if unit_symbol_info is None:
            raise SymbolNotFound(f"unit symbol for '{currency}' not found")

        unit_price = self.price_at(unit_symbol_info.name, time)['Close']
        if unit_symbol_info.currency_margin == self.unit:
            unit_price = 1. / unit_price

        return unit_price

Code:

sim = MtSimulator(
    unit='USD',
    balance=10000.,
    leverage=100.,
    stop_out_level=0.2,
    hedge=False,
)


sim.download_data(
    symbols=['EURUSD', 'GBPCAD', 'GBPUSD', 'USDCAD', 'USDCHF', 'GBPJPY', 'XAUUSD'],
    time_range=(
        datetime(2021, 5, 5, tzinfo=pytz.UTC),
        datetime(2021, 12, 5, tzinfo=pytz.UTC)
    ),
    timeframe=Timeframe.M5
)
sim.save_symbols("C:\\symbol.pkl")

Error:

  File "test.py", line 15, in <module>
    sim.download_data(
  File "D:\Porjects\rlmt\gym-mtsim-main\gym_mtsim\simulator\mt_simulator.py", line 59, in download_data
    si, df = retrieve_data(symbol, from_dt, to_dt, timeframe)
  File "D:\Porjects\rlmt\gym-mtsim-main\gym_mtsim\metatrader\api.py", line 20, in retrieve_data
    symbol_info = _get_symbol_info(symbol)
  File "D:\Porjects\rlmt\gym-mtsim-main\gym_mtsim\metatrader\api.py", line 53, in _get_symbol_info
    symbol_info = SymbolInfo(info)
  File "D:\Porjects\rlmt\gym-mtsim-main\gym_mtsim\metatrader\symbol.py", line 9, in __init__
    self.name: str = info.name
AttributeError: 'NoneType' object has no attribute 'name'

1 The _process_data() method takes the return value of _get_prices() as a feature, but the return value of _get_prices() is two columns of bar data which are close and open, and this are not features. Why? 2 _get_observation() returns a dict, but not a np.ndarray, for a typicle gym env， Shouldn't the observation returned by the step method be a np.ndarray? 3 I see many data structures in this framework use Dict, which should be to support multiple symbols? I only load data of a single symbol, so should I change the underlying data structure,? or just put a single key (the single symbol i focus on) in the dict.

Hi Amin,

On gym-anytradnig it's possible to override _process_data and add your own custom indicators (from Finta etc).

Is it possible to do the same with mtsim - or how should I be thinking about this? Currently I can't even see how to add and format my own training data.

Any ideas why the code below (it is basically the same as the code in the README.md file) would return different results each time it is run after the model has been trained? The trained model is no longer being updated and the input (trades) do not change between runs.

` model = A2C.load(modelName, env)

observation = env.reset()
while True:
    action, _states = model.predict(observation)
    observation, reward, done, info = env.step(action)
    
    state = env.render()
    z = calcsomevalue(state)
    if done:
       x  = getsomevalue(z)
       y = calsomevalue(z)
        print(<results>)
        break

`

this example does not open any trades

balance: 10000.0, equity: 10000.0, margin: 0.0
free_margin: 10000.0, margin_level: inf

just different from expected results you wrote

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
[<ipython-input-6-4ca9ec63cf36>](https://localhost:8080/#) in <module>()
     11 # env = gym.make('crypto-hedge-v0')
     12 
---> 13 model = A2C('MultiInputPolicy', env, verbose=0)
     14 model.learn(total_timesteps=1000)
     15 

2 frames
[/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/base_class.py](https://localhost:8080/#) in __init__(self, policy, env, learning_rate, policy_kwargs, tensorboard_log, verbose, device, support_multi_env, create_eval_env, monitor_wrapper, seed, use_sde, sde_sample_freq, supported_action_spaces)
    189                 assert np.all(
    190                     np.isfinite(np.array([self.action_space.low, self.action_space.high]))
--> 191                 ), "Continuous action space must have a finite lower and upper bound"
    192 
    193     @staticmethod

AssertionError: Continuous action space must have a finite lower and upper bound

Where can set the lower and upper bound? Thank you

Hello @AminHP , why is the low bound of balance, equity and features is -np.inf? see code:

        self.observation_space = spaces.Dict({
            'balance': spaces.Box(low=-np.inf, high=np.inf, shape=(1,)),
            'equity': spaces.Box(low=-np.inf, high=np.inf, shape=(1,)),
            'margin': spaces.Box(low=-np.inf, high=np.inf, shape=(1,)),
            'features': spaces.Box(low=-np.inf, high=np.inf, shape=self.features_shape),
            'orders': spaces.Box(
                low=-np.inf, high=np.inf,
                shape=(len(self.trading_symbols), self.symbol_max_orders, 3)
            )  # symbol, order_i -> [entry_price, volume, profit]
        })

shouldnt low bound start from zero? meaning its impossible for balance be below 0

Hello @AminHP , I noticed that you are using a constant leverage of 100 during the construction of the MtSimulator object, as you know leverage could be variant on the order level, means every order could have a different leverage, did you think of making leverage as part of the action space? I was thinking that allowing the agent to decide on the leverage (1x,5x,10x...etc) could allow it to make better decisions?