Sequence-based Target Coin Prediction for Cryptocurrency Pump-and-Dump (SIGMOD 2023)

This is the repo including dataset and code used in the paper "Sequence-based Target Coin Prediction for Cryptocurrency Pump-and-Dump".

Data Science Pipeline

The workflow mainly consists of two parts: data collection and target coin prediction.

Pump-and-dump Activity Logs (Jan. 1, 2019 to Jan. 15, 2022)

The P&D logs includes 1,335 samples and 709 P&Ds that we observed on Telegram. We will periodically update this dataset.

Getting Start

Since we have already collected P\&D log dataset and will periodically update it, you can skip the data collection part : )

0. Data Collection

First, we get seed channels verified by PumpOlymp and explore the pump channels.

• /Data/Telegram/Pump_channel1
• /Data/Telegram/Pump_channel2

python getchannelpost.py

python keyword_filte.py

python pumpmessagelabel.py
we have already labeled 5000+ samples.

python message_fg.py

python train_classifier.py

python predict_classifier.py

python sess_aggregate.py

python PD_label.py

python PDlog_clean.py

1. Target Coin Prediction

1.1 Feature Generation:

Two methods to generate features for Target Coin Prediction.

Method1: Generate features from P&D logs:

HOLD (still organizing this part of code because it involves feature collection from multiple sources,)

Mehod2: Download generated dataset from Good Drive

• trainsample

• testsample

cd TargetCoinPrediction
tar -xzvf train.tar.gz
tar -xzvf test.tar.gz

1.2 Model Training

Step1: Train SNN model

cd TargetCoinPrediction/SeqModel
python run_train.py  --model=snn \
                     --maxseqlength=8 \
                     --epoch=30 \
                     --batch_size=256 \
                     --learning_rate=1e-4 \
                     --dropout_rate=0.2
                     --do_train=True
                     --do_eval=False \
                     --checkpointDir=xxx

| Parameter | Description | |------------------|------------------------------------------------------------------------| | model | Model used for target coin prediction, options (snn, snnta, dnn) | | maxseqlength | The maximum length of P&D sequence, options 1~ 50, default=8. | | epochs | Number of training epochs, default = 30. | | batch_size | Batch size, default = 256. | | learning_rate | Learning rate for the optimizer (Adam), default = 5e-4. | | dropout_rate | Dropout Ratio for training, default = 0.2. | | do_train | Whether to do training or testing, default = True. | | do_eval | Whether to do training or testing, default = False. | | checkpointDir | Specify the directory to save the checkpoints. |

[//]: # (| init_seed | The initial seed, default = 1234. |)

Step2: Evaluate SNN model

python run_eval.py   --model=snn \
                     --maxseqlength=8 \
                     --epoch=1 \
                     --batch_size=256 \
                     --do_train=False
                     --do_eval=True \
                     --checkpointDir=xxx \
                     --init_seed=1234

Results on current dataset

We periodically update this table according to updated dataset.

| Metric | DNN | SNN | SNN_s | |---------|-------|-------|-------| | HR1 | 0.203 | 0.253 | 0.313 | | HR3 | 0.295 | 0.361 | 0.445 | | HR5 | 0.431 | 0.471 | 0.498 | | HR10 | 0.463 | 0.599 | 0.599 | | HR20 | 0.630 | 0.709 | 0.714 | | HR30 | 0.797 | 0.824 | 0.846 |

In this dataset we generate only 9 statistical features and coin id for pumped coin in sequence. The performance of SNN can be further improved by using more features for sequence.

Citation

If you find this repo useful, please cite our paper:

@article{hu2022sequence,
  title={Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump},
  author={Hu, Sihao and Zhang, Zhen and Lu, Shengliang and He, Bingsheng and Li, Zhao},
  journal={arXiv preprint arXiv:2204.12929},
  year={2022}
}