def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 4, 5]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF, 120)["ATR"][-1], 0))
df2.chart_type = Renko.PERIOD_CLOSE
renko_df = df2.get_ohlc_data()
renko_df["bar_num"] = np.where(
renko_df["uptrend"] == True, 1,
np.where(renko_df["uptrend"] == False, -1, 0)
) #Add a column where put 1 and -1 instead of the True/False we had in the uptrend column
for i in range(
1, len(renko_df["bar_num"])
): #Counts the amount of blocks generated in a trend Ex.: If we have 1,1,1,1,-1,-1,1,1... --> 1,2,3,4,-1,-2,1,2...
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i - 1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i - 1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
renko_df.drop_duplicates(
subset="date", keep="last", inplace=True
) #Some of the indexes are repeated (for example if there is a spike in a 5 min period and 2 ranko blocks are generated). Drop the duplicate indexes (same 5 min period), but keep the last
return renko_df
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 6, 5]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
df2 = Renko(df)
df2.brick_size = round(
ATR(DF, 120)["ATR"][-1], 0
) #We just want the last number of the column. We use ATR as the brick size in here but it can also be fix.
df2.chart_type = Renko.PERIOD_CLOSE #Renko box calcuation based on periodic close
#df2.chart_type = Renko.PRICE_MOVEMENT #Renko box calcuation based on price movement
renko_df = df2.get_ohlc_data(
) #Adds a column to df called "uptrend" with boolean values depending on whether the ranko chart goes up or down
return renko_df
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,4,5]]
df.columns = ["date","open","high","low","close","volume"]
df2 = Renko(df)
df2.brick_size = max(0.5,round(ATR(DF,120)["ATR"][-1],0))
df2.chart_type = Renko.PERIOD_CLOSE
renko_df = df2.get_ohlc_data()
renko_df["bar_num"] = np.where(renko_df["uptrend"]==True,1,np.where(renko_df["uptrend"]==False,-1,0))
for i in range(1,len(renko_df["bar_num"])):
if renko_df["bar_num"][i]>0 and renko_df["bar_num"][i-1]>0:
renko_df["bar_num"][i]+=renko_df["bar_num"][i-1]
elif renko_df["bar_num"][i]<0 and renko_df["bar_num"][i-1]<0:
renko_df["bar_num"][i]+=renko_df["bar_num"][i-1]
renko_df.drop_duplicates(subset="date",keep="last",inplace=True)
return renko_df
def ATR(DF,n=20):
temp = DF.copy()
temp["H-L"] = abs(temp["High"] - temp["Low"])
temp["H-PC"] = abs(temp["High"] - temp["Adj Close"].shift(1)) # high - previous day close
temp["L-PC"] = abs(temp["Low"] - temp["Adj Close"].shift(1))
temp["TR"] = temp[["H-L", "H-PC", "L-PC"]].max(axis=1, skipna = False)
temp["ATR"] = temp["TR"].rolling(n).mean()
return_val = temp.drop(["H-L","H-PC","L-PC"], axis=1)
return return_val
df.reset_index(inplace = True)
df = df.drop(["Close"], axis=1)
df.columns = ["date", "high", "low", "open", "volume", "close"]
renko_df = Renko(df)
renko_df.brick_size = 10
renko_df.chart_type = Renko.PERIOD_CLOSE
df2 = renko_df.get_ohlc_data()
df2.set_index("date", inplace = True)
def renko_DF(DF):
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,4,5]]
df.columns = ["date", "low", "high", "open", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF,120)["ATR"][-1],0))
