jsrimr

authorName: default
experimentName: example_mnist
trialConcurrency: 4
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai
trainingServicePlatform: local
#choice: true, false
useAnnotation: true
tuner:

jsrimr

fn = {'stochastic': get_stochastic, 'rsi': fnRSI,'macd': fnMACD, 'bollinger': get_bollinger_diffs}
self.get_derivative_diffs = fn.get(custom_args.environment)

jsrimr

def main():
    env = TradingEnv(custom_args=args, env_id='custom_trading_env', obs_data_len=obs_data_len, step_len=step_len, sample_len=sample_len,
                           df=df, fee=fee, initial_budget=1, n_action_intervals=n_action_intervals, deal_col_name='c', sell_at_end=True,
                           feature_names=['o', 'h','l','c','v',
                                          'num_trades', 'taker_base_vol'])
    agent = dqn_agent.Agent(action_size=2 * n_action_intervals + 1, obs_len=obs_data_len, num_features=env.reset().shape[-1], **hyperparams)
    agent.qnetwork_local.load_state_dict(torch.load(os.path.join(load_location, 'TradingGym_Rainbow_1000.pth'), map_location=device))
    agent.qnetwork_local.to(device)
    for eps in range(n_episode=500):
        next_state, reward, done, _ = env.step(agent.act(state))

jsrimr

def step(self):
  ...
  derivative_diff = self.get_derivative_diffs(self.df_sample.iloc[self.step_st: self.step_st + self.obs_len])
  self.fee_rate = np.clip( self.fee_rate * derivative_diff / self.previous_diff, self.min_fee_rate, self.max_fee_rate)
  ...

jsrimr

def fnMACD(m_Df, m_NumFast=12, m_NumSlow=26, m_NumSignal=9):
    EMAFast = m_Df['c'].ewm( span = m_NumFast, min_periods = m_NumFast - 1).mean()
    EMASlow = m_Df['c'].ewm( span = m_NumSlow, min_periods = m_NumSlow - 1).mean()
    MACD = EMAFast - EMASlow
    MACDSignal= MACD.ewm( span = m_NumSignal, min_periods = m_NumSignal-1).mean()
    MACDDiff= MACD - MACDSignal
    return MACDDiff.mean()

jsrimr

def get_bollinger_diffs(df, n=20, k=2):
    ma_n = df['c'].rolling(n).mean()
    Bol_upper = df['c'].rolling(n).mean() + k* df['c'].rolling(n).std()
    Bol_lower = df['c'].rolling(n).mean() - k* df['c'].rolling(n).std()
    return (Bol_upper - Bol_lower).mean()

jsrimr

def fnRSI(m_Df, m_N=15):
    m_Df = m_Df.c
    U = np.where(m_Df.diff(1) > 0, m_Df.diff(1), 0)
    D = np.where(m_Df.diff(1) < 0, m_Df.diff(1) *(-1), 0)

    AU = pd.DataFrame(U).rolling( window=m_N, min_periods=m_N).mean()
    AD = pd.DataFrame(D).rolling( window=m_N, min_periods=m_N).mean()
    RSI = AU.div(AD+AU)[0].mean()
    return RSI

jsrimr

def get_stochastic(df, n=15, m=5, t=3):
    # highest price during n days
    ndays_high = df.h.rolling(window=n, min_periods=1).max()
    # lowest price during n days
    ndays_low = df.l.rolling(window=n, min_periods=1).min()
    # Fast%K 
    kdj_k = ((df.c - ndays_low) / (ndays_high - ndays_low))
    # Fast%D (=Slow%K) 
    kdj_d = kdj_k.ewm(span=m).mean()
    # Slow%D 

jsrimr

class TradingEnv:
  def _long(self,): # buy
     ...
  def _long_cover(self, current_price_mean, current_mkt_position, action):  # sell possession
    ...
  def step(self, action):
    ...
    # process buy and sell action
    # update position of the agent
    # return next_state and reward

jsrimr

class Agent():
  def __init__(self, risk_aversion, **args):
    ...
  def model():
    ...
  def act(self, state, eps=0.):
  ...
    return model(state)
  def learn(self, experiences, is_weights, gamma):
  ...