From f0cb853984623467aedcab67d6004e7f176c3254 Mon Sep 17 00:00:00 2001
From: eric o meehan <eric@eom.dev>
Date: Mon, 9 Sep 2024 09:46:21 -0400
Subject: [PATCH] v0.0.5

---
 app.py | 48 +++++++++++++++++++++++++++++++++++-------------
 1 file changed, 35 insertions(+), 13 deletions(-)

diff --git a/app.py b/app.py
index 0e5685a..511e5be 100644
--- a/app.py
+++ b/app.py
@@ -1,14 +1,23 @@
 import krakenex
 import os
 import pandas
+import time
 
 from dotenv import load_dotenv
 from pykrakenapi import KrakenAPI
 from scipy.stats import linregress
 from time import sleep
 
-k = krakenex.API()
-k.load_key('kraken.key')
+load_dotenv()
+
+R_VALUE_TARGET = float(os.getenv('R_VALUE_TARGET'))
+INVESTMENT_COUNT = int(os.getenv('INVESTMENT_COUNT'))
+INVESTMENT_VOLUME = float(os.getenv('INVESTMENT_VOLUME'))
+
+k = krakenex.API(
+        os.getenv('KRAKEN_API_TOKEN'),
+        os.getenv('KRAKEN_API_SEC')
+        )
 kraken = KrakenAPI(k, tier='Pro')
 
 def filter_asset_pairs(tradable_asset_pairs):
@@ -18,8 +27,8 @@ def filter_asset_pairs(tradable_asset_pairs):
             ]
 
 def model_asset_pair(asset_pair):
-    sleep(1)
     ohlc = kraken.get_ohlc_data(asset_pair)[0]
+    sleep(kraken.factor)
     model = pandas.DataFrame({
         'open': linregress(ohlc['time'].astype(float), ohlc['open'].astype(float)),
         'high': linregress(ohlc['time'].astype(float), ohlc['high'].astype(float)),
@@ -39,7 +48,10 @@ def grade_models(models):
                 'stderr': models[model].stderr.mean()
                 } for model in models
             }).transpose()
-    filtered_models = model_averages[model_averages['r_value'] >= os.getenv('R_VALUE_TARGET')]
+    filtered_models = model_averages[
+            (model_averages['r_value'] >= R_VALUE_TARGET) &
+            (model_averages['slope'] > 0)
+            ]
     return filtered_models.sort_values(by='slope', ascending=False)
 
 def sell_asset(market, volume):
@@ -47,21 +59,26 @@ def sell_asset(market, volume):
             ordertype = 'market',
             type = 'sell',
             pair = market,
-            volume = volume
+            volume = volume,
+            validate = False
             )
+    sleep(kraken.factor)
+    print(f"Sold {volume} on {market}")
 
 def buy_asset(market, volume):
     kraken.add_standard_order(
             ordertype = 'market',
             type = 'buy',
             pair = market,
-            volume = volume
+            volume = volume,
+            validate = False
             )
+    print(f"Purchased {volume} on {market}")
+    sleep(kraken.factor)
 
-if __name__ == '__main__':
-    load_dotenv()
-
+def main():
     asset_pairs = filter_asset_pairs(kraken.get_tradable_asset_pairs())
+    sleep(kraken.factor)
     graded_models = grade_models(
             {asset_pair: model_asset_pair(asset_pair) for asset_pair in asset_pairs.index}
             )
@@ -72,10 +89,15 @@ if __name__ == '__main__':
         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'):
+    while len(account_balance := kraken.get_account_balance()) - 1 < INVESTMENT_COUNT: 
+        if account_balance.loc['ZUSD'].vol <= INVESTMENT_VOLUME:
             break
-        for market in graded_models:
+        for market in graded_models.index:
             if asset_pairs.loc[market].base not in account_balance.index:
-                buy_asset(market, os.getenv('INVESTMENT_VOLUME'))
+                ohlc, last = kraken.get_ohlc_data(market)
+                volume = INVESTMENT_VOLUME/ohlc.iloc[-1].close
+                buy_asset(market, volume)
                 break
+
+if __name__ == '__main__':
+    main()