diff --git a/tests/prices.py b/tests/prices.py
index 92144f9b2..2ddee2fa8 100644
--- a/tests/prices.py
+++ b/tests/prices.py
@@ -2,6 +2,9 @@
 
 import unittest
 
+import datetime as _dt
+import pytz as _tz
+
 class TestPriceHistory(unittest.TestCase):
 	def setUp(self):
 		pass
@@ -9,6 +12,62 @@ def setUp(self):
 	def tearDown(self):
 		pass
 
+	def test_duplicatingDaily(self):
+		tkrs = []
+		tkrs.append("IMP.JO")
+		tkrs.append("BHG.JO")
+		tkrs.append("SSW.JO")
+		tkrs.append("BP.L")
+		tkrs.append("INTC")
+		test_run = False
+		for tkr in tkrs:
+			dat = yf.Ticker(tkr)
+			tz = dat._get_ticker_tz()
+
+			dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
+			dt = dt_utc.astimezone(_tz.timezone(tz))
+			if dt.time() < _dt.time(17,0):
+				continue
+			test_run = True
+
+			df = dat.history(start=dt.date()-_dt.timedelta(days=7), interval="1d")
+
+			dt0 = df.index[-2]
+			dt1 = df.index[-1]
+			try:
+				self.assertNotEqual(dt0, dt1)
+			except:
+				print("Ticker = ", tkr)
+				raise
+
+		if not test_run:
+			self.skipTest("Skipping test_duplicatingDaily() because only expected to fail just after market close")
+
+	def test_duplicatingWeekly(self):
+		tkrs = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
+		test_run = False
+		for tkr in tkrs:
+			dat = yf.Ticker(tkr)
+			tz = dat._get_ticker_tz()
+
+			dt = _tz.timezone(tz).localize(_dt.datetime.now())
+			if dt.date().weekday() not in [1,2,3,4]:
+				continue
+			test_run = True
+
+			df = dat.history(start=dt.date()-_dt.timedelta(days=7), interval="1wk")
+			dt0 = df.index[-2]
+			dt1 = df.index[-1]
+			try:
+				self.assertNotEqual(dt0.week, dt1.week)
+			except:
+				print("Ticker={}: Last two rows within same week:".format(tkr))
+				print(df.iloc[df.shape[0]-2:])
+				raise
+
+		if not test_run:
+			self.skipTest("Skipping test_duplicatingWeekly() because not possible to fail Monday/weekend")
+
 	def test_intraDayWithEvents(self):
 		# Dividend release pre-market, doesn't merge nicely with intra-day data, so
 		# check still present
diff --git a/yfinance/base.py b/yfinance/base.py
index 4694a1add..9d758243f 100644
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -301,6 +301,8 @@ def history(self, period="1mo", interval="1d",
 
         tz_exchange = data["chart"]["result"][0]["meta"]["exchangeTimezoneName"]
 
+        quotes = utils.fix_Yahoo_returning_live_separate(quotes, params["interval"], tz_exchange)
+        
         # prepare index for combine:
         quotes.index = quotes.index.tz_localize("UTC").tz_convert(tz_exchange)
         splits.index = splits.index.tz_localize("UTC").tz_convert(tz_exchange)
diff --git a/yfinance/utils.py b/yfinance/utils.py
index 0871a2918..5ae434da2 100644
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -252,6 +252,51 @@ def parse_actions(data):
     return dividends, splits
 
 
+def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
+    # Yahoo bug fix. If market is open today then Yahoo normally returns 
+    # todays data as a separate row from rest-of week/month interval in above row. 
+    # Seems to depend on what exchange e.g. crypto OK.
+    # Fix = merge them together
+    n = quotes.shape[0]
+    if n > 1:
+        dt1 = quotes.index[n-1].tz_localize("UTC").tz_convert(tz_exchange)
+        dt2 = quotes.index[n-2].tz_localize("UTC").tz_convert(tz_exchange)
+        if interval in ["1wk", "1mo", "3mo"]:
+            if interval == "1wk":
+                last_rows_same_interval = dt1.year==dt2.year and dt1.week==dt2.week
+            elif interval == "1mo":
+                last_rows_same_interval = dt1.month==dt2.month
+            elif interval == "3mo":
+                last_rows_same_interval = dt1.year==dt2.year and dt1.quarter==dt2.quarter
+            if last_rows_same_interval:
+                # Last two rows are within same interval
+                idx1 = quotes.index[n-1]
+                idx2 = quotes.index[n-2]
+                if _np.isnan(quotes.loc[idx2,"Open"]):
+                    quotes.loc[idx2,"Open"] = quotes["Open"][n-1]
+                # Note: nanmax() & nanmin() ignores NaNs
+                quotes.loc[idx2,"High"] = _np.nanmax([quotes["High"][n-1], quotes["High"][n-2]])
+                quotes.loc[idx2,"Low"] = _np.nanmin([quotes["Low"][n-1], quotes["Low"][n-2]])
+                quotes.loc[idx2,"Close"] = quotes["Close"][n-1]
+                if "Adj High" in quotes.columns:
+                    quotes.loc[idx2,"Adj High"] = _np.nanmax([quotes["Adj High"][n-1], quotes["Adj High"][n-2]])
+                if "Adj Low" in quotes.columns:
+                    quotes.loc[idx2,"Adj Low"] = _np.nanmin([quotes["Adj Low"][n-1], quotes["Adj Low"][n-2]])
+                if "Adj Close" in quotes.columns:
+                    quotes.loc[idx2,"Adj Close"] = quotes["Adj Close"][n-1]
+                quotes.loc[idx2,"Volume"] += quotes["Volume"][n-1]
+                quotes = quotes.drop(quotes.index[n-1])
+
+        # Similar bug in daily data except most data is simply duplicated
+        # - exception is volume, *slightly* greater on final row (and matches website)
+        elif interval=="1d":
+            if dt1.date() == dt2.date():
+                # Last two rows are on same day. Drop second-to-last row
+                quotes = quotes.drop(quotes.index[n-2])
+
+    return quotes
+
+
 def safe_merge_dfs(df_main, df_sub, interval):
     # Carefully merge 'df_sub' onto 'df_main'
     # If naive merge fails, try again with reindexing df_sub: