Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
AI4Finance-Foundation
GitHub Repository: AI4Finance-Foundation/FinRL
 Path: blob/master/finrl/config.py
728 views
1
# directory

2
from __future__ import annotations

3


4
DATA_SAVE_DIR = "datasets"

5
TRAINED_MODEL_DIR = "trained_models"

6
TENSORBOARD_LOG_DIR = "tensorboard_log"

7
RESULTS_DIR = "results"

8


9
# date format: '%Y-%m-%d'

10
TRAIN_START_DATE = "2014-01-06"  # bug fix: set Monday right, start date set 2014-01-01 ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 1658 and the array at index 1 has size 1657

11
TRAIN_END_DATE = "2020-07-31"

12


13
TEST_START_DATE = "2020-08-01"

14
TEST_END_DATE = "2021-10-01"

15


16
TRADE_START_DATE = "2021-11-01"

17
TRADE_END_DATE = "2021-12-01"

18


19
# stockstats technical indicator column names

20
# check https://pypi.org/project/stockstats/ for different names

21
INDICATORS = [

22
    "macd",

23
    "boll_ub",

24
    "boll_lb",

25
    "rsi_30",

26
    "cci_30",

27
    "dx_30",

28
    "close_30_sma",

29
    "close_60_sma",

30
]

31


32


33
# Model Parameters

34
A2C_PARAMS = {"n_steps": 5, "ent_coef": 0.01, "learning_rate": 0.0007}

35
PPO_PARAMS = {

36
    "n_steps": 2048,

37
    "ent_coef": 0.01,

38
    "learning_rate": 0.00025,

39
    "batch_size": 64,

40
}

41
DDPG_PARAMS = {"batch_size": 128, "buffer_size": 50000, "learning_rate": 0.001}

42
TD3_PARAMS = {"batch_size": 100, "buffer_size": 1000000, "learning_rate": 0.001}

43
SAC_PARAMS = {

44
    "batch_size": 64,

45
    "buffer_size": 100000,

46
    "learning_rate": 0.0001,

47
    "learning_starts": 100,

48
    "ent_coef": "auto_0.1",

49
}

50
ERL_PARAMS = {

51
    "learning_rate": 3e-5,

52
    "batch_size": 2048,

53
    "gamma": 0.985,

54
    "seed": 312,

55
    "net_dimension": 512,

56
    "target_step": 5000,

57
    "eval_gap": 30,

58
    "eval_times": 64,  # bug fix:KeyError: 'eval_times' line 68, in get_model model.eval_times = model_kwargs["eval_times"]

59
}

60
RLlib_PARAMS = {"lr": 5e-5, "train_batch_size": 500, "gamma": 0.99}

61


62


63
# Possible time zones

64
TIME_ZONE_SHANGHAI = "Asia/Shanghai"  # Hang Seng HSI, SSE, CSI

65
TIME_ZONE_USEASTERN = "US/Eastern"  # Dow, Nasdaq, SP

66
TIME_ZONE_PARIS = "Europe/Paris"  # CAC,

67
TIME_ZONE_BERLIN = "Europe/Berlin"  # DAX, TECDAX, MDAX, SDAX

68
TIME_ZONE_JAKARTA = "Asia/Jakarta"  # LQ45

69
TIME_ZONE_SELFDEFINED = "xxx"  # If neither of the above is your time zone, you should define it, and set USE_TIME_ZONE_SELFDEFINED 1.

70
USE_TIME_ZONE_SELFDEFINED = 0  # 0 (default) or 1 (use the self defined)

71


72
# parameters for data sources

73
ALPACA_API_KEY = "xxx"  # your ALPACA_API_KEY

74
ALPACA_API_SECRET = "xxx"  # your ALPACA_API_SECRET

75
ALPACA_API_BASE_URL = "https://paper-api.alpaca.markets"  # alpaca url

76
BINANCE_BASE_URL = "https://data.binance.vision/"  # binance url

77


78