FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥

Last update: Jan 08, 2023

Overview

FinRL-Meta: A Universe of Market Environments.

FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users can use FinRL-Meta as the metaverse of their financial environments.

FinRL-Meta separates financial data processing from the design pipeline of DRL-based strategy and provides open-source data engineering tools for financial big data.
FinRL-Meta provides hundreds of market environments for various trading tasks.
FinRL-Meta enables multiprocessing simulation and training by exploiting thousands of GPU cores.

Also called Neo_FinRL: Near real-market Environments for data-driven Financial Reinforcement Learning.

Our Goals

To reduce the simulation-reality gap: existing works use backtesting on historical data, while the real performance may be quite different when applying the algorithms to paper/live trading.
To reduce the data pre-processing burden, so that quants can focus on developing and optimizing strategies.
To provide benchmark performance and facilitate fair comparisons, providing a standardized environment will allow researchers to evaluate different strategies in the same way. Also, it would help researchers to better understand the “black-box” nature (deep neural network-based) of DRL algorithms.

Design Principles

Plug-and-Play (PnP): Modularity; Handle different markets (say T0 vs. T+1)
Completeness and universal: Multiple markets; Various data sources (APIs, Excel, etc); User-friendly variables.
Avoid hard-coded parameters
Closing the sim-real gap using the “training-testing-trading” pipeline: simulation for training and connecting real-time APIs for testing/trading.
Efficient data sampling: accelerate the data sampling process is the key to DRL training! From the ElegantRL project. we know that multi-processing is powerful to reduce the training time (scheduling between CPU + GPU).
Transparency: a virtual env that is invisible to the upper layer
Flexibility and extensibility: Inheritance might be helpful here

Overview

We utilize a layered structure in FinRL-metaverse, as shown in the figure above. FinRL-metaverse consists of three layers: data layer, environment layer, and agent layer. Each layer executes its functions and is independent. Meanwhile, layers interact through end-to-end interfaces to implement the complete workflow of algorithm trading.

DataOps

DataOps is a series of principles and practices to improve the quality and reduce the cycle time of data science. It inherits the ideas of Agile development, DevOps, and lean manufacturing and applies them to the data science and machine learning field. FinRL-Meta follows the DataOps paradigm.

Supported Data Sources:

Data Source	Type	Range and Frequency	Request Limits	Raw Data	Preprocessed Data
Yahoo! Finance	US Securities	Frequency-specific, 1min	2,000/hour	OHLCV	Prices&Indicators
CCXT	Cryptocurrency	API-specific, 1min	API-specific	OHLCV	Prices&Indicators
WRDS.TAQ	US Securities	2003-now, 1ms	5 requests each time	Intraday Trades	Prices&Indicators
Alpaca	US Stocks, ETFs	2015-now, 1min	Account-specific	OHLCV	Prices&Indicators
RiceQuant	CN Securities	2005-now, 1ms	Account-specific	OHLCV	Prices&Indicators
JoinQuant	CN Securities	2005-now, 1min	3 requests each time	OHLCV	Prices&Indicators
QuantConnect	US Securities	1998-now, 1s	NA	OHLCV	Prices&Indicators

Plug-and-Play

In the development pipeline, we separate market environments from the data layer and the agent layer. Any DRL agent can be directly plugged into our environments, then trained and tested. Different agents/algorithms can be compared by running on the same benchmark environment for fair evaluations.

A demonstration notebook for plug-and-play with ElegantRL, Stable Baselines3 and RLlib: Play and Play with DRL Agents

"Training-Testing-Trading" Pipeline

A DRL agent learns by interacting with the training environment, is validated in the validation environment for parameter tuning. Then, the agent is tested in historical datasets (backtesting). Finally, the agent will be deployed in paper trading or live trading markets.

This pipeline solves the information leakage problem because the trading data are never leaked when training/tuning the agents.

Such a unified pipeline allows fair comparisons among different algorithms and strategies.

Our Vision

For future work, we plan to build a multi-agent-based market simulator that consists of over ten thousands of agents, namely, a FinRL-Metaverse. First, FinRL-Metaverse aims to build a universe of market environments, like the XLand environment (source) and planet-scale climate forecast (source) by DeepMind. To improve the performance for large-scale markets, we will employ GPU-based massive parallel simulation as Isaac Gym (source). Moreover, it will be interesting to explore the deep evolutionary RL framework (source) to simulate the markets. Our final goal is to provide insights into complex market phenomena and offer guidance for financial regulations through FinRL-Metaverse.

Citing FinRL-Meta

@article{finrl_meta_2021,
    author = {Liu, Xiao-Yang and Rui, Jingyang and Gao, Jiechao and Yang, Liuqing and Yang, Hongyang and Wang, Zhaoran and Wang, Christina Dan and Guo Jian},
    title   = {{FinRL-Meta}: Data-Driven Deep ReinforcementLearning in Quantitative Finance},
    journal = {Data-Centric AI Workshop, NeurIPS},
    year    = {2021}
}

Collaborators

Comments

ValueError: If using all scalar values, you must pass an index using binance processor

I'm using the meta Dataprocessor

import meta
from meta.data_processor import DataProcessor

When I run the following :


#Set constants
#
LIST_OF_SYMBOLS = ['ADAUSDT' ,'ATOMUSDT' ,'BNBUSDT', 'BTCUSDT' ,'DOTUSDT' ,'ETCUSDT', 'ETHUSDT','LINKUSDT', 'LTCUSDT' ,'SOLUSDT' ,'XMRUSDT' ,'XRPUSDT']
#Set time interval
TIME_INTERVAL = '1D'

#Training start
START_TRAIN =  '2018-01-1'


#Training end
END_TRAIN = '2020-12-1'

#Trading start
START_TRADE = '2020-12-1'

#Trading end
END_TRADE = '2022-06-01'

#List of technical indicators
TECHNICAL_INDICATORS = ['rsi',
                        'cci',
                        'macd',
                        'macd_signal',
                        'macd_hist',
                        'dx'
                             ]  

if_vix = False

processorObj = DataProcessor(data_source = 'binance', start_date= START_TRAIN, end_date =END_TRAIN, time_interval=TIME_INTERVAL) 
processorObj.download_data(LIST_OF_SYMBOLS)
processorObj.clean_data()
processorObj.add_technical_indicator(TECHNICAL_INDICATORS)
frame = processorObj.dataframe

frame.head()

I get :


ValueError                                Traceback (most recent call last)
[<ipython-input-26-256be0b7d088>](https://localhost:8080/#) in <module>
     30 
     31 processorObj = DataProcessor(data_source = 'binance', start_date= START_TRAIN, end_date =END_TRAIN, time_interval=TIME_INTERVAL)
---> 32 processorObj.download_data(LIST_OF_SYMBOLS)
     33 processorObj.clean_data()
     34 processorObj.add_technical_indicator(TECHNICAL_INDICATORS)

7 frames
[/FinRL-Meta/meta/data_processor.py](https://localhost:8080/#) in download_data(self, ticker_list)
     84 
     85     def download_data(self, ticker_list):
---> 86         self.processor.download_data(ticker_list=ticker_list)
     87         self.dataframe = self.processor.dataframe
     88 

[/FinRL-Meta/meta/data_processors/binance.py](https://localhost:8080/#) in download_data(self, ticker_list)
     53             final_df = pd.DataFrame()
     54             for i in ticker_list:
---> 55                 hist_data = self.dataframe_with_limit(symbol=i)
     56                 df = hist_data.iloc[:-1].dropna()
     57                 df["tic"] = i

[/FinRL-Meta/meta/data_processors/binance.py](https://localhost:8080/#) in dataframe_with_limit(self, symbol)
    183         while True:
    184 
--> 185             new_df = self.get_binance_bars(last_datetime, symbol)
    186             if new_df is None:
    187                 break

[/FinRL-Meta/meta/data_processors/binance.py](https://localhost:8080/#) in get_binance_bars(self, last_datetime, symbol)
    121           #r = requests.get(self.url, params=req_params)
    122           #print(r.text)
--> 123 
    124 
    125         df = pd.DataFrame(requests.get(self.url, params=req_params).json())        if df.empty:

[/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py](https://localhost:8080/#) in __init__(self, data, index, columns, dtype, copy)
    612         elif isinstance(data, dict):
    613             # GH#38939 de facto copy defaults to False only in non-dict cases
--> 614             mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
    615         elif isinstance(data, ma.MaskedArray):
    616             import numpy.ma.mrecords as mrecords

[/usr/local/lib/python3.7/dist-packages/pandas/core/internals/construction.py](https://localhost:8080/#) in dict_to_mgr(data, index, columns, dtype, typ, copy)
    463 
    464     return arrays_to_mgr(
--> 465         arrays, data_names, index, columns, dtype=dtype, typ=typ, consolidate=copy
    466     )
    467 

[/usr/local/lib/python3.7/dist-packages/pandas/core/internals/construction.py](https://localhost:8080/#) in arrays_to_mgr(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)
    117         # figure out the index, if necessary
    118         if index is None:
--> 119             index = _extract_index(arrays)
    120         else:
    121             index = ensure_index(index)

[/usr/local/lib/python3.7/dist-packages/pandas/core/internals/construction.py](https://localhost:8080/#) in _extract_index(data)
    623 
    624         if not indexes and not raw_lengths:
--> 625             raise ValueError("If using all scalar values, you must pass an index")
    626 
    627         if have_series:

ValueError: If using all scalar values, you must pass an index

Even when I try defining the list as ['ADAUSDT'] ,I still get the error

@zhumingpassional @BruceYanghy @XiaoYangLiu-FinRL

Please assist

opened by Daiiszuki 14

actions on plug and play DRL notebook

Hello, I'm a beginner of RL. I wonder that if we can see the actions with the ElegentRL's DRL_prediction function ? (Return actions in [-1,0 1], just like the sb3's DRL_prediction function). It will be helpful if someone share their idea, thanks!

Besides, I wonder that is it suitable to trade index (like DJI) as single stock with the env in plug_and_play_DRL notebook ?
discussion

opened by wezzyzzs 8
Add tests
I have added several tests:

Added testcase for Binance

Added testcase for clean_data()

added testcase for add_technical_indicator(both stockstats and TALib)
opened by eyast 7
AttributeError: 'NoneType' object has no attribute 'config'

Hi, train RLlib come up with following error

~/anaconda3/lib/python3.7/site-packages/ray/worker.py in get(object_refs, timeout) 1625 raise value.as_instanceof_cause() 1626 else: -> 1627 raise value 1628 1629 if is_individual_id:

RayActorError: The actor died because of an error raised in its creation task, ray::RolloutWorker.init() (pid=43634, ip=192.168.1.81) AttributeError: 'NoneType' object has no attribute 'config'

During handling of the above exception, another exception occurred:

ray::RolloutWorker.init() (pid=43634, ip=192.168.1.81) File "/home/reza/anaconda3/lib/python3.7/site-packages/ray/rllib/evaluation/rollout_worker.py", line 565, in init devices = get_tf_gpu_devices() File "/home/reza/anaconda3/lib/python3.7/site-packages/ray/rllib/utils/tf_ops.py", line 54, in get_gpu_devices devices = tf.config.experimental.list_physical_devices() AttributeError: 'NoneType' object has no attribute 'config'

it seems that is due to Tensorflow TensorBoard ... any idea, how can we sort it out? regards
bug

opened by uh-reza 7

ModuleNotFoundError not found in FinRL_PortfolioAllocation_NeurIPS_2020.ipynb

While running 2.3. Import Packages got this error

ModuleNotFoundError                       Traceback (most recent call last)
[<ipython-input-2-65f21698d972>](https://localhost:8080/#) in <module>()
      9 from finrl import config
     10 from finrl import config_tickers
---> 11 from finrl.finrl_meta.preprocessor.yahoodownloader import YahooDownloader
     12 from finrl.finrl_meta.preprocessor.preprocessors import FeatureEngineer, data_split
     13 from finrl.finrl_meta.env_portfolio_allocation.env_portfolio import StockPortfolioEnv

ModuleNotFoundError: No module named 'finrl.finrl_meta'

Actually, it appears that all of these statements are failing.

from finrl.finrl_meta.preprocessor.yahoodownloader import YahooDownloader
from finrl.finrl_meta.preprocessor.preprocessors import FeatureEngineer, data_split
from finrl.finrl_meta.env_portfolio_allocation.env_portfolio import StockPortfolioEnv
from finrl.finrl_meta.data_processor import DataProcessor
from finrl.finrl_meta.data_processors.processor_yahoofinance import YahooFinanceProcessor

I've tried running this

and changing the from statement to

from finrl_meta.preprocessor.yahoodownloader import YahooDownloader

and

from finrl-meta.preprocessor.yahoodownloader import YahooDownloader

still no luck. 😞

Finally tried

from finrl.meta.preprocessor.yahoodownloader import YahooDownloader

That seemed to have done the trick. 😄

bug

opened by kabua 6

Create create_env.py

I highly suggest for simplifying and easier debugging and better readability and understandability of the codes, the environment creation gets it's own function and gets separated from the train and test functions. One of the prominent advantages of FinRL-Meta is it's modularity and having different independent layers, which the proposed change helps going further in this direction.

opened by mhdmyz 5
Vix
removed the clean data code from the basic_processor because it's a duplicate. The cleaning steps are individual to each processor and should be / are done there.

made it possible to use download_data multiple times (for vix) uses append if self.dataframe is not empty.

changed some pandas operation to inplace

reimplemented / prepared the add_vix function.

Tested it with yahoo finance processor. There is a problem though: The clean_data function removes the vix column again. Wanted to fix that, the code is pretty complicate though and it's probably better the original coder of it fixes it. Also the clean_data from yahoo finance uses backward fill? This introduces lookahead. Should be addressed too.

# if close on start date is NaN, fill data with first valid close # and set volume to 0.

So this PR isn't ready and needs more work. The other processor needs to be tested. It provides a solid foundation for making the vix work again though. Hope it helps.
suggestion
opened by cryptocoinserver 5
Can not find the Demo_China_A_share_market.ipynb

Thank you for your good work.

For some reason, I could not find the link for "Demo_China_A_share_market.ipynb".

The full link was: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/Demo_China_A_share_market.ipynb

Can you please provide some hints?

Thank you.
bug

opened by tairen99 4
Error in Demo_FinRL_Meta_Integrate_Trends_data_to_DOW_Jones.ipynb

Hello,

First issue

if I run the jupyter notebook Demo_FinRL_Meta_Integrate_Trends_data_to_DOW_Jones.ipynb on my local computer, I receive warnings in 2 cells. In the cell # !gdown --id "1sp11dtAJGGqC-3UdSn774ZD1zWCsqbn4" !gdown --id "1m63ncE-BYlS77u5ejYTte9Nmh35DWhzp"

I receive the warning that the command "gdown" is either written wrongly or couldnt be found. And for the cell

!unzip "/content/Pytrends.zip"

I get the warning

unzip: cannot find or open /content/Pytrends.zip, /content/Pytrends.zip.zip or /content/Pytrends.zip.ZIP. I assume its due to these warnings that later on in the cell

user_df = get_user_df() len(user_df)

I get the error FileNotFoundError: [WinError 3] Das System kann den angegebenen Pfad nicht finden: 'Pytrends_Data'

which means that "Pytrends_data" could not be found by the system.

Second Issue

I tried running the code on Google Colab instead, however here I received a different error. In the cell

train_env_instance = get_train_env(TRAIN_START_DATE, TRAIN_END_DATE, ticker_list, data_source, time_interval,model_name, env,info_col) val_env_instance = get_test_env(VAL_START_DATE, VAL_END_DATE, ticker_list, data_source, time_interval, info_col, env, model_name)

I got the error

` in download_data(self, ticker_list, start_date, end_date, time_interval) 33 end_date = end_date, 34 time_interval = time_interval) ---> 35 self.dataframe = self.processor.dataframe 36 37 def clean_data(self):

AttributeError: 'YahooFinanceProcessor' object has no attribute 'dataframe'`

I would be very grateful for help for either the errors on my local computer or the error in the google colab. Thank you in advance for any help!
bug

opened by Kartolon 4

NameError: name 'MACD' is not defined

Demo_MultiCrypto_Trading.ipynb

Binance successfully connected
Adding self-defined technical indicators is NOT supported yet.
Use default: MACD, RSI, CCI, DX.

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-15-29348b501caa> in <module>()
     11       erl_params=ERL_PARAMS,
     12       break_step=5e4,
---> 13       if_vix=False
     14       )

3 frames

/FinRL-Meta/finrl_meta/data_processors/processor_binance.py in add_technical_indicator(self, df, tech_indicator_list)
     48         for i in df.tic.unique():
     49             tic_df = df[df.tic==i]
---> 50             tic_df['macd'], tic_df['macd_signal'], tic_df['macd_hist'] = MACD(tic_df['close'], fastperiod=12, 
     51                                                                                 slowperiod=26, signalperiod=9)
     52             tic_df['rsi'] = RSI(tic_df['close'], timeperiod=14)

NameError: name 'MACD' is not defined

Caused by the commented line here: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/3a8fdcd7ad1e3ad8e3dbbc9a647cb5eef3769507/finrl_meta/data_processors/processor_binance.py#L6

good first issue discussion

opened by cryptocoinserver 4

Error with TRAIN and TEST interval

In "Demo_MultiCrypto_Trading" notebook I try to change the interval of data eg: TRAIN_START_DATE = '2020-08-01' -- original START DATE=2021-09-01 TRAIN_END_DATE = '2021-09-20'

TEST_START_DATE = '2021-09-21' TEST_END_DATE = '2021-09-30'

with a different timeframe of "60m" instead of "5m". It gives me back the following error: ValueError: If using all scalar values, you must pass an index

How can I solve it please? Thx
bug

opened by matti410 4
FinRL_PaperTrading_Demo np.hstack()

When executing the example here without any modifications except for including Alpaca API key, I get the following error:

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 1192 and the array at index 1 has size 1177

which is coming from this line here. Anyone else run into this issue?

opened by josh0tt 0
How to connect one of FinRL algorithm to the real market ? (and execute trades)

Hello,

Thanks for this wonderful library.

I would like to connect one of the RL algos (say the Stock_NeurIPS2018_SB3) to a real broker.

Do you recommend any Python Library to do so ? Or any other way ?

I was thinking of using the metatrader 5 Python module.
help_wanted

opened by aymeric75 1

[DEBUGGING HELP] ValueError: could not broadcast input array from shape (14,6) into shape (22,14)

In refernce to https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/1-Introduction/FinRL_PortfolioAllocation_NeurIPS_2020.ipynb

The train data is shaped as (7812, 19)

Passing the data to the env runs without any errors

cryptoEnv = cryptoPortfolioAllocationEnvironment(dataFrame=trainData, **envKwargs)

And when I call cryptoEnv.observation_space, the shape is (22, 14), which assume is a combination of the price and indicators:

14 tickers, 8 indicators

running activeEnv, _ = cryptoEnv.stableBaselineEnv()

returns


ValueError                                Traceback (most recent call last)
[<ipython-input-68-f822f4852cfe>](https://localhost:8080/#) in <module>
----> 1 activeEnv, _ = cryptoEnv.stableBaselineEnv()

2 frames
[<ipython-input-63-fd656b920b2f>](https://localhost:8080/#) in stableBaselineEnv(self)
    189     def stableBaselineEnv(self):
    190       sb = DummyVecEnv([lambda: self])
--> 191       obs = sb.reset()
    192       return sb, obs
    193 

[/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py](https://localhost:8080/#) in reset(self)
     62         for env_idx in range(self.num_envs):
     63             obs = self.envs[env_idx].reset()
---> 64             self._save_obs(env_idx, obs)
     65         return self._obs_from_buf()
     66 

[/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py](https://localhost:8080/#) in _save_obs(self, env_idx, obs)
     92         for key in self.keys:
     93             if key is None:
---> 94                 self.buf_obs[key][env_idx] = obs
     95             else:
     96                 self.buf_obs[key][env_idx] = obs[key]

ValueError: could not broadcast input array from shape (14,6) into shape (22,14)

What am I missing ?

Please let me know if you require any additional info.The function to generate the env is as follows

def stableBaselineEnv(self):
      sb = DummyVecEnv([lambda: self])
      obs = sb.reset()
      return sb, obs

bug

opened by Daiiszuki 48

FinRL_PaperTrading_Demo.ipynb - errors on trading operation

Environment: Win10 Chrome Colab File at https://colab.research.google.com/github/AI4Finance-Foundation/FinRL-Tutorials/blob/master/3-Practical/FinRL_PaperTrading_Demo.ipynb

No change to this file other than Alpaca credentials required.

Function call: paper_trading_erl = AlpacaPaperTrading(ticker_list = DOW_30_TICKER, time_interval = '1Min', drl_lib = 'elegantrl', agent = 'ppo', cwd = './papertrading_erl_retrain', net_dim = ERL_PARAMS['net_dimension'], state_dim = state_dim, action_dim= action_dim, API_KEY = API_KEY, API_SECRET = API_SECRET, API_BASE_URL = API_BASE_URL, tech_indicator_list = INDICATORS, turbulence_thresh=30, max_stock=1e2) paper_trading_erl.run()

Error Output: load actor from: ./papertrading_erl_retrain/actor.pth Waiting for market to open... 0 minutes til market open. Market opened. Exception in thread Thread-12: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-13: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-14: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-15: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-16: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-17: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-18: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-19: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-20: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-21: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-22: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-23: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-24: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Exception in thread Thread-25: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 182, in trade state = self.get_state() File "", line 247, in get_state tech_indicator_list=self.tech_indicator_list) File "/usr/local/lib/python3.7/dist-packages/finrl/meta/data_processors/processor_alpaca.py", line 355, in fetch_latest_data raise ValueError ValueError

Succesfully add technical indicators Successfully transformed into array 30 Exception in thread Thread-26: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 186, in trade s_tensor = torch.as_tensor((state,), device=self.device) AttributeError: 'AlpacaPaperTrading' object has no attribute 'device'

Succesfully add technical indicators Successfully transformed into array 30 Exception in thread Thread-27: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 186, in trade s_tensor = torch.as_tensor((state,), device=self.device) AttributeError: 'AlpacaPaperTrading' object has no attribute 'device'

Succesfully add technical indicators Successfully transformed into array 30 Exception in thread Thread-28: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 186, in trade s_tensor = torch.as_tensor((state,), device=self.device) AttributeError: 'AlpacaPaperTrading' object has no attribute 'device'

Succesfully add technical indicators Successfully transformed into array 30 Exception in thread Thread-29: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 186, in trade s_tensor = torch.as_tensor((state,), device=self.device) AttributeError: 'AlpacaPaperTrading' object has no attribute 'device'

Succesfully add technical indicators Successfully transformed into array 30 Exception in thread Thread-30: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "", line 186, in trade s_tensor = torch.as_tensor((state,), device=self.device) AttributeError: 'AlpacaPaperTrading' object has no attribute 'device'
bug

opened by marcipops 9

Releases(v0.3.5)

v0.3.5(Jun 26, 2022)

Source code(tar.gz)
Source code(zip)

Owner

AI4Finance Foundation

An open-source community sharing AI tools for finance.

GitHub Repository

The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

92 Nov 30, 2022

FinRL­-Meta: A Universe for Data­-Driven Financial Reinforcement Learning. 🔥

Related tags

Overview

FinRL-Meta: A Universe of Market Environments.

Outline

Our Goals

Design Principles

Overview

DataOps

Plug-and-Play

"Training-Testing-Trading" Pipeline

Our Vision

Citing FinRL-Meta

Collaborators

Comments

Releases(v0.3.5)

v0.3.5(Jun 26, 2022)

Owner

AI4Finance Foundation

The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

AI virtual gym is an AI program which can be used to exercise and can be used to see if we are doing the exercises

The self-supervised goal reaching benchmark introduced in Discovering and Achieving Goals via World Models

Open Source Differentiable Computer Vision Library for PyTorch

Multi Camera Calibration

Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective

pytorch bert intent classification and slot filling

pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

Linear algebra python - Number of operations and problems in Linear Algebra and Numerical Linear Algebra

Single Image Random Dot Stereogram for Tensorflow

Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution.

Gradient Step Denoiser for convergent Plug-and-Play

This repo is to present various code demos on how to use our Graph4NLP library.

Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

Research code for the paper "Variational Gibbs inference for statistical estimation from incomplete data".

GraPE is a Rust/Python library for high-performance Graph Processing and Embedding.

An AutoML Library made with Optuna and PyTorch Lightning

ExCon: Explanation-driven Supervised Contrastive Learning

Recurrent Scale Approximation (RSA) for Object Detection

FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥