Add scripts to create indicators and sequences, and download crypto data

create_indicators.py

@@ -0,0 +1,40 @@
+import pandas as pd
+import numpy as np
+
+df = pd.read_csv('candles.csv')
+
+
+from talib import RSI, BBANDS, MACD, ATR, EMA, SMA
+
+
+# group by market
+grouped = df.groupby('market')
+
+# for each market calculate the indicators and add them to the dataframe
+for market, group in grouped:
+    # calculate the indicators
+    print('Calculating indicators for', market)
+    df.loc[group.index,'rsi'] = RSI(group['close'], timeperiod=14)
+    
+    upper, middle, lower = BBANDS(group['close'], timeperiod=20)
+    df.loc[group.index,'bb_upper'] = upper
+    df.loc[group.index,'bb_middle'] = middle
+    df.loc[group.index,'bb_lower'] = lower
+
+    macd, macdsignal, macdhist = MACD(group['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+    df.loc[group.index,'macd'] = macd
+    df.loc[group.index,'macdsignal'] = macdsignal
+    df.loc[group.index,'macdhist'] = macdhist
+
+    df.loc[group.index,'atr'] = ATR(group['high'], group['low'], group['close'], timeperiod=14)
+
+    df.loc[group.index,'ema'] = EMA(group['close'], timeperiod=30)
+    df.loc[group.index,'sma'] = SMA(group['close'], timeperiod=30)
+
+# drop the rows with NaN values
+df = df.dropna()
+
+    
+# save the dataframe to a new file
+print(df.tail())
+df.to_csv('indicators.csv', index=False)

create_sequences.py

@@ -0,0 +1,64 @@
+
+
+#  data = data[['market', 'time', 'open', 'high', 'low', 'close', 'volume']]
+# transform this dataset so to 'market', 'start', 'column', 'value1', 'value2', 'value3', 'value(n)'
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+columns = ['open','close','volume', 'rsi', 'sma']
+window_size = 10
+
+def create_sequences(df, columns=columns, window_size=window_size):
+    # group by market
+    grouped = df.groupby('market')
+
+    # create a list of dataframes
+    dfs = []
+
+    for name, group in tqdm(grouped):
+        # create a new dataframe
+        new_df = pd.DataFrame()
+        new_df['market'] = name
+        # create a list of lists
+        sequences = []
+        # only include the close column
+        # iterate over the rows of the dataframe
+        for i in range(len(group) - window_size):
+            # create a sequence
+            sequence = group.iloc[i:i+window_size][columns].values
+            # transpose the sequence so that it is a column
+            sequence = sequence.T
+
+            # create a dataframe from the sequence
+            sequence = pd.DataFrame(sequence)
+
+            # add the market, time, column_name to the sequence
+            sequence['market'] = name
+            sequence['time'] = group.iloc[i+window_size]['time']
+            sequence['column'] = columns
+
+            # set market, time as the first columns and index
+            sequence = sequence.set_index(['market', 'time', 'column'])
+
+            # add the sequence to the list of sequences
+            sequences.append(sequence)
+        if len(sequences) == 0:
+            continue
+        # create a dataframe from the list of lists
+        new_df = pd.concat(sequences)
+        # add the dataframe to the list of dataframes
+        dfs.append(new_df)
+    
+    # concatenate the list of dataframes
+    final_df = pd.concat(dfs)
+
+    return final_df
+
+df = pd.read_csv('indicators.csv')
+
+# create the sequences
+sequences = create_sequences(df, columns=columns, window_size=15)
+
+# save the sequences to a new file
+sequences.to_csv('sequences.csv')

crypto_data.py

@@ -0,0 +1,69 @@
+from datasets import DatasetBuilder, DownloadManager, DatasetInfo, BuilderConfig, SplitGenerator, Split, Features, Value
+import pandas as pd
+
+# Define custom configurations for the dataset
+class CryptoDataConfig(BuilderConfig):
+    def __init__(self, features, **kwargs):
+        super().__init__(**kwargs)
+        self.features = features
+
+class CryptoDataDataset(DatasetBuilder):
+    # Define different dataset configurations here
+    BUILDER_CONFIGS = [
+        CryptoDataConfig(
+            name="candles",
+            description="This configuration includes open, high, low, close, and volume.",
+            features=Features({
+                "date": Value("string"),
+                "open": Value("float"),
+                "high": Value("float"),
+                "low": Value("float"),
+                "close": Value("float"),
+                "volume": Value("float")
+            })
+        ),
+        CryptoDataConfig(
+            name="indicators",
+            description="This configuration extends basic CryptoDatas with RSI, SMA, and EMA indicators.",
+            features=Features({
+                "date": Value("string"),
+                "open": Value("float"),
+                "high": Value("float"),
+                "low": Value("float"),
+                "close": Value("float"),
+                "volume": Value("float"),
+                "rsi": Value("float"),
+                "sma": Value("float"),
+                "ema": Value("float")
+            })
+        ),
+    ]
+
+    def _info(self):
+        return DatasetInfo(
+            description=f"CryptoData dataset for {self.config.name}",
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="https://hub.huggingface.co/datasets/sebdg/crypto_data",
+            citation="No citation for this dataset."
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        # Here, you can define how to split your dataset (e.g., into training, validation, test)
+        # This example assumes a single CSV file without predefined splits.
+        # You can modify this method if you have different needs.
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"filepath": "indicators.csv"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        # Here, we open the provided CSV file and yield each row as a single example.
+        with open(filepath, encoding="utf-8") as csv_file:
+            data = pd.read_csv(csv_file)
+            for id, row in data.iterrows():
+                # Select features based on the dataset configuration
+                features = {feature: row[feature] for feature in self.config.features if feature in row}
+                yield id, features

download.py

@@ -41,15 +41,18 @@ print('Data downloaded and saved to assets.csv and markets.csv')
 if not os.path.exists('candles'):
     os.makedirs('candles')
 
-# for market in markets['market']:
-#     print('Downloading', market)
-#     url = f'https://api.bitvavo.com/v2/{market}/candles?interval=1d&limit=1440'
-#     response = requests.get(url)
-#     data = response.json()
-#     #print(data)
-#     data = pd.DataFrame(data, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
-#     data.to_csv(f'candles/{market}.csv', index=False)
-#     time.sleep(0.5)
+for market in markets['market']:
+    print('Downloading', market)
+    url = f'https://api.bitvavo.com/v2/{market}/candles?interval=1d&limit=1440'
+    response = requests.get(url)
+    data = response.json()
+    #print(data)
+    data = pd.DataFrame(data, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
+    data['market'] = market
+    # set market as the first column
+    data = data[['market', 'time', 'open', 'high', 'low', 'close', 'volume']]
+    data.to_csv(f'candles/{market}.csv', index=False)
+    time.sleep(0.5)
 
 # print('Ticker data downloaded')