remove test trades

app.py

@@ -45,7 +45,7 @@ class color:
     UNDERLINE = '\033[4m'
     END = '\033[0m'
     
-@st.experimental_memo    
+@st.cache_data    
 def print_PL(amnt, thresh, extras = "" ):
     if amnt > 0:
         return color.BOLD + color.GREEN + str(amnt) + extras + color.END
@@ -56,7 +56,7 @@ def print_PL(amnt, thresh, extras = "" ):
     else:
         return str(amnt + extras)
     
-@st.experimental_memo      
+@st.cache_data      
 def get_headers(logtype):
     otimeheader = ""
     cheader = ""
@@ -108,7 +108,7 @@ def get_headers(logtype):
     
     return otimeheader.lower(), cheader.lower(), plheader.lower(), fmat
     
-@st.experimental_memo    
+@st.cache_data    
 def get_coin_info(df_coin, principal_balance,plheader):
     numtrades = int(len(df_coin))
     numwin = int(sum(df_coin[plheader] > 0))
@@ -129,13 +129,13 @@ def get_coin_info(df_coin, principal_balance,plheader):
     
     return numtrades, numwin, numloss, winrate, pfactor, cum_PL, cum_PL_perc, mean_PL, mean_PL_perc
 
-@st.experimental_memo
+@st.cache_data  
 def get_hist_info(df_coin, principal_balance,plheader):
     numtrades = int(len(df_coin))
     numwin = int(sum(df_coin[plheader] > 0))
     numloss = int(sum(df_coin[plheader] < 0))
     if numtrades != 0:
-        winrate = int(np.round(100*numwin/numtrades,4))
+        winrate = np.round(100*numwin/numtrades,4)
     else: 
         winrate = np.nan
     
@@ -147,7 +147,7 @@ def get_hist_info(df_coin, principal_balance,plheader):
         pfactor = np.nan
     return numtrades, numwin, numloss, winrate, pfactor
 
-@st.experimental_memo
+@st.cache_data  
 def get_rolling_stats(df, lev, otimeheader, days):
     max_roll = (df[otimeheader].max() - df[otimeheader].min()).days
     
@@ -162,13 +162,13 @@ def get_rolling_stats(df, lev, otimeheader, days):
     else:
         rolling_perc = np.nan
     return 100*rolling_perc
-@st.experimental_memo
+@st.cache_data  
 def cc_coding(row):
     return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2022-12-16 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
 def ctt_coding(row):
     return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2023-01-02 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
 
-@st.experimental_memo
+@st.cache_data
 def my_style(v, props=''):
     props = 'color:red' if v < 0 else 'color:green'
     return props
@@ -217,18 +217,20 @@ def load_data(filename, account, exchange, otimeheader, fmat):
     else: 
         newdf = pd.DataFrame([], columns=['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %'])
     
-    if account == 'Pure Bread Test':
+    if account == 'Pure Bread':
         tvdata = pd.read_csv('pb-history-old.csv',header = 0).drop('Unnamed: 0', axis=1)
+    elif account == 'Pumpernickel':
+        tvdata = pd.read_csv('pn-history-old.csv',header = 0).drop('Unnamed: 0', axis=1)
     else:
         tvdata = pd.DataFrame([])
     if tvdata.empty:
         df = newdf
     else:
         df = pd.concat([tvdata, newdf], ignore_index =True)
-        df = df.sort_values('Entry Date', ascending = True)
-        df.index = range(len(df))
-        df.Trade = df.index + 1 
-    
+    df = df.sort_values('Entry Date', ascending = True)
+    df.index = range(len(df))
+    df.Trade = df.index + 1 
+        
     dateheader = 'Date'
     theader = 'Time'
 
@@ -329,11 +331,11 @@ def runapp() -> None:
             if bot_selections == "Pumpernickel":
                 lev_cap = 5
                 dollar_cap = 1000000000.00
-                data = load_data('history.csv', 'Pumpernickel Test', 'Bybit Futures', otimeheader, fmat)
+                data = load_data('history.csv', 'Pumpernickel', 'Bybit Futures', otimeheader, fmat)
             if bot_selections == "Pure Bread":
                 lev_cap = 5
                 dollar_cap = 1000000000.00
-                data = load_data('history_new.csv', 'Pure Bread Test', 'Bybit Futures', otimeheader, fmat)
+                data = load_data('history.csv', 'Pure Bread', 'Bybit Futures', otimeheader, fmat)
             df = data.copy(deep=True)
 
             dateheader = 'Date'
@@ -385,7 +387,8 @@ def runapp() -> None:
 
             if no_errors:
                 if bot_selections == "Pumpernickel":
-                    dca_map = {1: 1/3, 2: 1/3, 3: 1/3}
+                    dca_map = {1: 1/5, 2: 1/5, 3: 1/5, 4: 1/5, 5: 1/5} #for unequal dca amounts
+                    signal_map = {'Long': 1, 'Short':-1}  
                     df['DCA %'] = df['DCA'].map(dca_map)
                     df['Calculated Return %'] = (df['DCA %'])*(df['Signal'].map(signal_map)*(df['Sell Price']-df['Buy Price'])/df['Buy Price']-2*fees) #accounts for fees on open and close of trade 
                     df['DCA'] = np.floor(df['DCA'].values)
@@ -409,6 +412,7 @@ def runapp() -> None:
                     df['Compounded Return'] = df['Return Per Trade'].cumprod()
                     df['New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df['Compounded Return']]
                     df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
+                
                 df['Net P/L Per Trade'] = drop_frac_cents((df['Return Per Trade']-1)*df['Balance used in Trade'])
                 df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
 
@@ -562,7 +566,7 @@ def runapp() -> None:
                                 st.metric(
                                 "Max Drawdown",
                                     "",#f"{np.round(100*max_draw/principal_balance,2)/100*principal_balance:.2f}",
-                                    f"{np.round(max_draw)}%",
+                                    f"{np.round(max_draw,2)}%",
                                 )
 
             if bot_selections == "Pumpernickel":