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How fix this error? I am using NNTC_pv_LSTM + ShortTradeDurHyperOptLoss #28

Closed maledicente closed 6 months ago

maledicente commented 1 year ago

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:17 • -:--:--WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:17 • -:--:--WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:17 • -:--:--WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:18 • -:--:-- 2023-05-08 22:19:29,633 - freqtrade - ERROR - Fatal exception! joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/home/ftuser/.local/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py", line 391, in _process_worker call_item = call_queue.get(block=True, timeout=timeout) File "/usr/local/lib/python3.10/multiprocessing/queues.py", line 122, in get return _ForkingPickler.loads(res) File "/home/ftuser/.local/lib/python3.10/site-packages/joblib/externals/loky/cloudpickle_wrapper.py", line 43, in _reconstruct_wrapper obj = loads(_pickled_object) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/pickle_utils.py", line 48, in deserialize_model_from_bytecode raise e File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/pickle_utils.py", line 46, in deserialize_model_from_bytecode model = saving_lib.load_model(filepath, safe_mode=False) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/saving_lib.py", line 277, in load_model raise e File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/saving_lib.py", line 242, in load_model model = deserialize_keras_object( File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/serialization_lib.py", line 508, in deserialize_keras_object instance.compile_from_config(compile_config) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/engine/training.py", line 3392, in compile_from_config self.optimizer.build(self.trainable_variables) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/optimizers/legacy/optimizer_v2.py", line 984, in getattribute raise e File "/home/ftuser/.local/lib/python3.10/site-packages/keras/optimizers/legacy/optimizer_v2.py", line 974, in getattribute return super().getattribute(name) AttributeError: 'Adam' object has no attribute 'build' """

nateemma commented 1 year ago

Can you send me the full output please? From what I can see here, it looks like there is an error loading the model. Did you change anything related to the neural network algorithm or the number of inputs?

Thanks

Phil

On Tue, May 9, 2023 at 2:09 AM Luiz Paulo Nievola @.***> wrote:

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:17 • -:--:--WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:17 • -:--:--WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:17 • -:--:--WARNING:tensorflow:From /freqtrade/user_data/strategies/ClassifierKeras.py:44: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead.

Epochs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/1000 0% • 0:00:18 • -:--:-- 2023-05-08 22:19:29,633 - freqtrade - ERROR - Fatal exception! joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "/home/ftuser/.local/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py", line 391, in _process_worker call_item = call_queue.get(block=True, timeout=timeout) File "/usr/local/lib/python3.10/multiprocessing/queues.py", line 122, in get return _ForkingPickler.loads(res) File "/home/ftuser/.local/lib/python3.10/site-packages/joblib/externals/loky/cloudpickle_wrapper.py", line 43, in _reconstruct_wrapper obj = loads(_pickled_object) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/pickle_utils.py", line 48, in deserialize_model_from_bytecode raise e File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/pickle_utils.py", line 46, in deserialize_model_from_bytecode model = saving_lib.load_model(filepath, safe_mode=False) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/saving_lib.py", line 277, in load_model raise e File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/saving_lib.py", line 242, in load_model model = deserialize_keras_object( File "/home/ftuser/.local/lib/python3.10/site-packages/keras/saving/serialization_lib.py", line 508, in deserialize_keras_object instance.compile_from_config(compile_config) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/engine/training.py", line 3392, in compile_from_config self.optimizer.build(self.trainable_variables) File "/home/ftuser/.local/lib/python3.10/site-packages/keras/optimizers/legacy/optimizer_v2.py", line 984, in getattribute raise e File "/home/ftuser/.local/lib/python3.10/site-packages/keras/optimizers/legacy/optimizer_v2.py", line 974, in getattribute return super().getattribute(name) AttributeError: 'Adam' object has no attribute 'build' """

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maledicente commented 1 year ago

I'm using docker in oracle-cloud/ubuntu, i didn't change any party of the code.

mmsquantum commented 1 year ago

lmao, well there is your problem

nateemma commented 1 year ago

Hmm, it works for me.

Can you send the full output please? I especially need to see the text at the top of the run (it shows version numbers etc.)

Also, can you run the following command please (and send me the output)?:

h5ls -r user_data/strategies/binanceus/models/NNTC_pv_LSTM/NNTC_pv_LSTM.h5

(I'm assuming you are running from the binanceus exchange, if not, change that to the location you are using)

if you don't have h5ls installed, you can get it using the following command:

sudo apt-get install libhdf5-dev

Thanks,

Phil

On Wed, May 10, 2023 at 3:14 PM Luiz Paulo Nievola @.***> wrote:

I'm using docker in oracle-cloud/ubuntu, i didn't change any party of the code.

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maledicente commented 1 year ago

h5ls NNTC_pv_LSTM_ACH.h5

(base) ubuntu@bot-swarm:~/freqtrade/user_data/strategies/models/NNTC_pv_LSTM$ h5ls -r NNTC_pv_LSTM_ACH.h5 / Group /model_weights Group /model_weights/dense Group /model_weights/dense/dense Group /model_weights/dense/dense/bias:0 Dataset {3} /model_weights/dense/dense/kernel:0 Dataset {128, 3} /model_weights/dropout Group /model_weights/lstm Group /model_weights/lstm/lstm Group /model_weights/lstm/lstm/lstm_cell Group /model_weights/lstm/lstm/lstm_cell/bias:0 Dataset {512} /model_weights/lstm/lstm/lstm_cell/kernel:0 Dataset {64, 512} /model_weights/lstm/lstm/lstm_cell/recurrent_kernel:0 Dataset {128, 512} /model_weights/top_level_model_weights Group /optimizer_weights Group /optimizer_weights/Adam Group /optimizer_weights/Adam/dense Group /optimizer_weights/Adam/dense/bias Group /optimizer_weights/Adam/dense/bias/m:0 Dataset {3} /optimizer_weights/Adam/dense/bias/v:0 Dataset {3} /optimizer_weights/Adam/dense/kernel Group /optimizer_weights/Adam/dense/kernel/m:0 Dataset {128, 3} /optimizer_weights/Adam/dense/kernel/v:0 Dataset {128, 3} /optimizer_weights/Adam/iter:0 Dataset {SCALAR} /optimizer_weights/Adam/lstm Group /optimizer_weights/Adam/lstm/lstm_cell Group /optimizer_weights/Adam/lstm/lstm_cell/bias Group /optimizer_weights/Adam/lstm/lstm_cell/bias/m:0 Dataset {512} /optimizer_weights/Adam/lstm/lstm_cell/bias/v:0 Dataset {512} /optimizer_weights/Adam/lstm/lstm_cell/kernel Group /optimizer_weights/Adam/lstm/lstm_cell/kernel/m:0 Dataset {64, 512} /optimizer_weights/Adam/lstm/lstm_cell/kernel/v:0 Dataset {64, 512} /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel Group /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel/m:0 Dataset {128, 512} /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel/v:0 Dataset {128, 512}

nateemma commented 1 year ago

the model parameters look OK. Can you send me the full output of a test run please?

You can also just generate your own model as follows - but this will take a long time (and see notes at the end):

remove existing models

rm -r user_data/strategies/models/NNTC_pv_LSTM

download pair data

zsh user_data/strategies/scripts/download.sh -n 750 binanceus

generate new model

zsh user_data/strategies/scripts/test_strat.sh -n 750 binanceus NNTC_pv_LSTM

run hyperopt (optional, but suggested)

zsh user_data/strategies/scripts/hyp_strat.sh -n 90 -e 100 binanceus NNTC_pv_LSTM

Notes:

Thanks,

Phil

On Sat, May 13, 2023 at 5:56 AM Luiz Paulo Nievola @.***> wrote:

h5ls NNTC_pv_LSTM_ACH.h5

(base) @.***:~/freqtrade/user_data/strategies/models/NNTC_pv_LSTM$ h5ls -r NNTC_pv_LSTM_ACH.h5 / Group /model_weights Group /model_weights/dense Group /model_weights/dense/dense Group /model_weights/dense/dense/bias:0 Dataset {3} /model_weights/dense/dense/kernel:0 Dataset {128, 3} /model_weights/dropout Group /model_weights/lstm Group /model_weights/lstm/lstm Group /model_weights/lstm/lstm/lstm_cell Group /model_weights/lstm/lstm/lstm_cell/bias:0 Dataset {512} /model_weights/lstm/lstm/lstm_cell/kernel:0 Dataset {64, 512} /model_weights/lstm/lstm/lstm_cell/recurrent_kernel:0 Dataset {128, 512} /model_weights/top_level_model_weights Group /optimizer_weights Group /optimizer_weights/Adam Group /optimizer_weights/Adam/dense Group /optimizer_weights/Adam/dense/bias Group /optimizer_weights/Adam/dense/bias/m:0 Dataset {3} /optimizer_weights/Adam/dense/bias/v:0 Dataset {3} /optimizer_weights/Adam/dense/kernel Group /optimizer_weights/Adam/dense/kernel/m:0 Dataset {128, 3} /optimizer_weights/Adam/dense/kernel/v:0 Dataset {128, 3} /optimizer_weights/Adam/iter:0 Dataset {SCALAR} /optimizer_weights/Adam/lstm Group /optimizer_weights/Adam/lstm/lstm_cell Group /optimizer_weights/Adam/lstm/lstm_cell/bias Group /optimizer_weights/Adam/lstm/lstm_cell/bias/m:0 Dataset {512} /optimizer_weights/Adam/lstm/lstm_cell/bias/v:0 Dataset {512} /optimizer_weights/Adam/lstm/lstm_cell/kernel Group /optimizer_weights/Adam/lstm/lstm_cell/kernel/m:0 Dataset {64, 512} /optimizer_weights/Adam/lstm/lstm_cell/kernel/v:0 Dataset {64, 512} /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel Group /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel/m:0 Dataset {128, 512} /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel/v:0 Dataset {128, 512}

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nateemma commented 1 year ago

or, if you wait a week or so, I am in the middle of re-writing parts of NNTC and will re-generate models. It takes me about a week to generate all the models though 😡

Thanks,

Phil

On Sun, May 14, 2023 at 2:46 PM Phil Price @.***> wrote:

the model parameters look OK. Can you send me the full output of a test run please?

You can also just generate your own model as follows - but this will take a long time (and see notes at the end):

remove existing models

rm -r user_data/strategies/models/NNTC_pv_LSTM

download pair data

zsh user_data/strategies/scripts/download.sh -n 750 binanceus

generate new model

zsh user_data/strategies/scripts/test_strat.sh -n 750 binanceus NNTC_pv_LSTM

run hyperopt (optional, but suggested)

zsh user_data/strategies/scripts/hyp_strat.sh -n 90 -e 100 binanceus NNTC_pv_LSTM

Notes:

  • you have model_per_pair set to True. You can speed up training by setting this to False (maybe try this with the existing model). This will then use a single model (user_data/strategies/models/NNTC_pv_LSTM/NNTC_pv_LSTM.h5) for all pairs
  • I typically train models over ~2 years of data (750 days). If you just want to see it runs, use a shorter time period; it will work, but just won't be a good model

Thanks,

Phil

On Sat, May 13, 2023 at 5:56 AM Luiz Paulo Nievola < @.***> wrote:

h5ls NNTC_pv_LSTM_ACH.h5

(base) @.***:~/freqtrade/user_data/strategies/models/NNTC_pv_LSTM$ h5ls -r NNTC_pv_LSTM_ACH.h5 / Group /model_weights Group /model_weights/dense Group /model_weights/dense/dense Group /model_weights/dense/dense/bias:0 Dataset {3} /model_weights/dense/dense/kernel:0 Dataset {128, 3} /model_weights/dropout Group /model_weights/lstm Group /model_weights/lstm/lstm Group /model_weights/lstm/lstm/lstm_cell Group /model_weights/lstm/lstm/lstm_cell/bias:0 Dataset {512} /model_weights/lstm/lstm/lstm_cell/kernel:0 Dataset {64, 512} /model_weights/lstm/lstm/lstm_cell/recurrent_kernel:0 Dataset {128, 512} /model_weights/top_level_model_weights Group /optimizer_weights Group /optimizer_weights/Adam Group /optimizer_weights/Adam/dense Group /optimizer_weights/Adam/dense/bias Group /optimizer_weights/Adam/dense/bias/m:0 Dataset {3} /optimizer_weights/Adam/dense/bias/v:0 Dataset {3} /optimizer_weights/Adam/dense/kernel Group /optimizer_weights/Adam/dense/kernel/m:0 Dataset {128, 3} /optimizer_weights/Adam/dense/kernel/v:0 Dataset {128, 3} /optimizer_weights/Adam/iter:0 Dataset {SCALAR} /optimizer_weights/Adam/lstm Group /optimizer_weights/Adam/lstm/lstm_cell Group /optimizer_weights/Adam/lstm/lstm_cell/bias Group /optimizer_weights/Adam/lstm/lstm_cell/bias/m:0 Dataset {512} /optimizer_weights/Adam/lstm/lstm_cell/bias/v:0 Dataset {512} /optimizer_weights/Adam/lstm/lstm_cell/kernel Group /optimizer_weights/Adam/lstm/lstm_cell/kernel/m:0 Dataset {64, 512} /optimizer_weights/Adam/lstm/lstm_cell/kernel/v:0 Dataset {64, 512} /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel Group /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel/m:0 Dataset {128, 512} /optimizer_weights/Adam/lstm/lstm_cell/recurrent_kernel/v:0 Dataset {128, 512}

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maledicente commented 1 year ago

There are anything wrong with the backtest

output.txt

nateemma commented 1 year ago

The results look too good, which usually means that you have something forward-looking in your technical indicators. Did you add any?

Thanks

Phil

On Tue, May 16, 2023 at 3:49 PM Luiz Paulo Nievola @.***> wrote:

There are anything wrong with the backtest

output.txt https://github.com/nateemma/strategies/files/11492609/output.txt

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nateemma commented 1 year ago

Actually, I'm seeing really good results too. I'll see if I can track down what's going on. You might want to try a dry run to see how it performs against live data

Thanks,

Phil

On Thu, May 18, 2023 at 8:53 PM Phil Price @.***> wrote:

The results look too good, which usually means that you have something forward-looking in your technical indicators. Did you add any?

Thanks

Phil

On Tue, May 16, 2023 at 3:49 PM Luiz Paulo Nievola < @.***> wrote:

There are anything wrong with the backtest

output.txt https://github.com/nateemma/strategies/files/11492609/output.txt

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nateemma commented 1 year ago

OK, I cannot find any lookahead bias. However, I notice that the returns are extremely sensitive to the time period used. My suspicion is that the performance is good because your training and test data overlap (i.e. you are testing with some of the data that you also used for training). I will take a look at excluding the last 30 days (for example) of data from any training/verification and see how that works...

Cheers,

Phil

On Fri, May 19, 2023 at 10:49 AM Phil Price @.***> wrote:

Actually, I'm seeing really good results too. I'll see if I can track down what's going on. You might want to try a dry run to see how it performs against live data

Thanks,

Phil

On Thu, May 18, 2023 at 8:53 PM Phil Price @.***> wrote:

The results look too good, which usually means that you have something forward-looking in your technical indicators. Did you add any?

Thanks

Phil

On Tue, May 16, 2023 at 3:49 PM Luiz Paulo Nievola < @.***> wrote:

There are anything wrong with the backtest

output.txt https://github.com/nateemma/strategies/files/11492609/output.txt

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maledicente commented 1 year ago

Hallo, I have seen this happen with IchiV1 and others strategy. "excluding the last 30 days (for example) of data from any training/verification and see how that works..." -> I do the same, 80 train / 20 test

https://github.com/freqtrade/freqtrade/issues/6209

I'm using: Mode: Dry-run Exchange: binance Market: spot Stake per trade: unlimited USDT Max open Trades: 6 Minimum ROI: {'0': 0.06} Entry strategy: {"order_book_top": 1, "price_last_balance": 0.0, "check_depth_of_market": {"enabled": false, "bids_to_ask_delta": 1}, "price_side": "other", "use_order_book": false} Exit strategy: {"use_order_book": false, "order_book_top": 1, "price_side": "other"} Stoploss: -0.99 Position adjustment: Off Timeframe: 5m Strategy: NNTC_pv_LSTM Current state: running

ROI: Closed trades ∙ -4.517 USDT (-0.04%) (-0.45 Σ%) ∙ -22.45 BRL ROI: All trades ∙ -11.081 USDT (-0.10%) (-1.11 Σ%) ∙ -55.073 BRL Total Trade Count: 68 Bot started: 2023-05-20 04:22:04 First Trade opened: 3 days ago (2023-05-20 04:30:37) Latest Trade opened: 40 minutes ago (2023-05-23 12:10:44) Win / Loss: 29 / 35 Avg. Duration: 5:56:25 Best Performing: ACH/USDT: 5.37% Trading volume: 21750.449 USDT Profit factor: 0.87 Max Drawdown: 1.86% (18.717 USDT)

nateemma commented 1 year ago

I already reserve the last 20% of the specified time period for testing, so if you run backtest somewhere within that test region, you should get realistic results.

I also plan (if I ever get enough free time) to try reserving even more data so that the model doesn't even see that data during training, even for validation.

I have been dry running this strategy for a few days, and it's actually doing OK (up 1.43%) From the way it is trading (quick in & out), it might be better suited for a 'scalping' approach, i.e. lots of open trades for small amounts.

FYI, I am currently working on a bug fix for labelling (there was a condition where events could be tagged as both hold and sell). I am re-gnerating the models and will push as soon as that is done (probably in a few days as there are lots of models to generate)

Thanks,

Phil