v0.0.4

.env_example

@@ -1,3 +1,6 @@
 KRAKEN_URL = "https://api.kraken.com"
 KRAKEN_API_TOKEN = 
 KRAKEN_API_SEC = 
+R_VALUE_TARGET = 
+INVESTMENT_COUNT = 
+INVESTMENT_VOLUME = 

app.py

@@ -1,4 +1,5 @@
 import krakenex
+import os
 import pandas
 
 from dotenv import load_dotenv
@@ -10,19 +11,71 @@ k = krakenex.API()
 k.load_key('kraken.key')
 kraken = KrakenAPI(k, tier='Pro')
 
-def get_markets_by_asset(asset):
+def filter_asset_pairs(tradable_asset_pairs):
-    tradable_asset_pairs = kraken.get_tradable_asset_pairs()
     return tradable_asset_pairs[
-            ((tradable_asset_pairs['base'] == asset | tradable_asset_pairs['quote'] == asset)
+            (tradable_asset_pairs['quote'] == 'ZUSD') &
-                & (tradable_asset_pairs['status'] == 'online'))
+            (tradable_asset_pairs['status'] == 'online')
             ]
 
-def get_ohlc_linear_regression(ohlc):
+def model_asset_pair(asset_pair):
-    regressions = pandas.DataFrame({
+    sleep(1)
+    ohlc = kraken.get_ohlc_data(asset_pair)[0]
+    model = pandas.DataFrame({
         'open': linregress(ohlc['time'].astype(float), ohlc['open'].astype(float)),
         'high': linregress(ohlc['time'].astype(float), ohlc['high'].astype(float)),
         'low': linregress(ohlc['time'].astype(float), ohlc['low'].astype(float)),
         'close': linregress(ohlc['time'].astype(float), ohlc['close'].astype(float))
         }).transpose()
-    regressions.columns = ['slope', 'intercept', 'r_value', 'p_value', 'stderr']
+    model.columns = ['slope', 'intercept', 'r_value', 'p_value', 'stderr']
-    return regressions
+    return model
+
+def grade_models(models):
+    model_averages = pandas.DataFrame({
+            model: {
+                'slope': models[model].slope.mean(),
+                'intercept': models[model].intercept.mean(),
+                'r_value': models[model].r_value.mean(),
+                'p_value': models[model].p_value.mean(),
+                'stderr': models[model].stderr.mean()
+                } for model in models
+            }).transpose()
+    filtered_models = model_averages[model_averages['r_value'] >= os.getenv('R_VALUE_TARGET')]
+    return filtered_models.sort_values(by='slope', ascending=False)
+
+def sell_asset(market, volume):
+    kraken.add_standard_order(
+            ordertype = 'market',
+            type = 'sell',
+            pair = market,
+            volume = volume
+            )
+
+def buy_asset(market, volume):
+    kraken.add_standard_order(
+            ordertype = 'market',
+            type = 'buy',
+            pair = market,
+            volume = volume
+            )
+
+if __name__ == '__main__':
+    load_dotenv()
+
+    asset_pairs = filter_asset_pairs(kraken.get_tradable_asset_pairs())
+    graded_models = grade_models(
+            {asset_pair: model_asset_pair(asset_pair) for asset_pair in asset_pairs.index}
+            )
+
+    account_balance = kraken.get_account_balance()
+    for asset in account_balance.index.drop('ZUSD'):
+        market = asset_pairs[asset_pairs['base'] == asset]
+        if market.index[0] not in graded_models[:5].index:
+            sell_asset(market, account_balance.loc[asset].vol)
+
+    while len(account_balance := kraken.get_account_balance()) - 1 < os.getenv('INVESTMENT_COUNT'): 
+        if account_balance.loc['ZUSD'] <= os.getenv('INVESTMENT_VOLUME'):
+            break
+        for market in graded_models:
+            if asset_pairs.loc[market].base not in account_balance.index:
+                buy_asset(market, os.getenv('INVESTMENT_VOLUME'))
+                break