From 005937e3e06c354214f4c2e7363510838ebfe1b1 Mon Sep 17 00:00:00 2001 From: Joshua Attridge Date: Fri, 23 Feb 2024 21:05:18 +0000 Subject: [PATCH 1/8] Cleaned up FVG data --- smartmoneyconcepts/smc.py | 15 ++++++++++----- tests/SMC.test_binance.py | 23 ++++++++++++----------- tests/test_binance.png | Bin 99743 -> 46300 bytes 3 files changed, 22 insertions(+), 16 deletions(-) diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index 96c2ecc..a68fbcd 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -10,7 +10,6 @@ def inputvalidator(input_="ohlc"): def dfcheck(func): @wraps(func) def wrap(*args, **kwargs): - args = list(args) i = 0 if isinstance(args[0], pd.DataFrame) else 1 @@ -53,8 +52,7 @@ def decorate(cls): @apply(inputvalidator(input_="ohlc")) class smc: - - __version__ = "0.0.13" + __version__ = "0.0.14" atr_multiplier = 1.5 range_percent = 0.01 @@ -79,8 +77,9 @@ def fvg(cls, ohlc: DataFrame) -> Series: & (ohlc["close"] < ohlc["open"]) ), np.where(ohlc["close"] > ohlc["open"], 1, -1), - 0, + np.nan, ) + top = np.where( ohlc["close"] > ohlc["open"], ohlc["low"].shift(-1), ohlc["low"].shift(1) ) @@ -88,6 +87,10 @@ def fvg(cls, ohlc: DataFrame) -> Series: ohlc["close"] > ohlc["open"], ohlc["high"].shift(1), ohlc["high"].shift(-1) ) + nan_indices = np.isnan(fvg) + top[nan_indices] = np.nan + bottom[nan_indices] = np.nan + mitigated_index = np.zeros(len(ohlc), dtype=np.int32) for i in np.where(fvg != 0)[0]: mask = np.zeros(len(ohlc), dtype=np.bool_) @@ -99,6 +102,9 @@ def fvg(cls, ohlc: DataFrame) -> Series: j = np.argmax(mask) + i + 2 mitigated_index[i] = j + # if fvg is np.nan then set mitigated_index to np.nan + mitigated_index = np.where(np.isnan(fvg), np.nan, mitigated_index) + # create a series for each of the keys in the dictionary fvg = pd.Series(fvg, name="FVG") top = pd.Series(top, name="Top") @@ -160,7 +166,6 @@ def highs_lows(cls, ohlc: DataFrame) -> Series: def swing_tops_bottoms( cls, ohlc: DataFrame, swing_length: int = swing_length ) -> Series: - swing_tops_bottoms = np.zeros(len(ohlc), dtype=np.int32) swing_type = 0 prev_swing_type = 0 diff --git a/tests/SMC.test_binance.py b/tests/SMC.test_binance.py index 2efc34b..1be3649 100644 --- a/tests/SMC.test_binance.py +++ b/tests/SMC.test_binance.py @@ -4,6 +4,8 @@ import os from binance.client import Client from datetime import datetime +import numpy as np +import time sys.path.append(os.path.abspath("../")) from smartmoneyconcepts.smc import smc @@ -26,7 +28,7 @@ def import_data(symbol, start_str, timeframe): df = import_data("BTCUSDT", "2021-01-01", "1d") -df = df.iloc[-500:] +df = df.iloc[-100:] fig = go.Figure( data=[ @@ -42,11 +44,11 @@ def import_data(symbol, start_str, timeframe): def add_FVG(fig): + start = time.time() fvg_data = smc.fvg(df) - # plot a rectangle for each fvg for i in range(len(fvg_data["FVG"])): - if fvg_data["FVG"][i] != 0: - x1 = ( + if not np.isnan(fvg_data["FVG"][i]): + x1 = int( fvg_data["MitigatedIndex"][i] if fvg_data["MitigatedIndex"][i] != 0 else len(df) - 1 @@ -185,7 +187,6 @@ def add_OB(fig): def add_VOB(fig): - ob_data = smc.vob(df) def format_volume(volume): @@ -328,12 +329,12 @@ def add_liquidity(fig): fig = add_FVG(fig) -fig = add_highs_lows(fig) -fig = add_swing_tops_bottoms(fig) -fig = add_bos_choch(fig) -fig = add_OB(fig) -fig = add_VOB(fig) -fig = add_liquidity(fig) +# fig = add_highs_lows(fig) +# fig = add_swing_tops_bottoms(fig) +# fig = add_bos_choch(fig) +# fig = add_OB(fig) +# fig = add_VOB(fig) +# fig = add_liquidity(fig) fig.update_layout(xaxis_rangeslider_visible=False) fig.update_layout(showlegend=False) fig.update_layout(margin=dict(l=0, r=0, b=0, t=0)) diff --git a/tests/test_binance.png b/tests/test_binance.png index 4f0afb8b60049e6cfa10e009e6e0ffad3f93a5e9..4503e740b38a9833d472701a337a804585b20a62 100644 GIT binary patch literal 46300 zcmeFZcT|(x_BIOAK|rKRRZ*&d3P^8K1!*F^i%N+!K?ooqUAjn@2e 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z6geED9H+V6M#s)wy%*@X_b9OQa<+}-5498YgT>0Nq!x?jW!kwFaX?53vHAk z1>A-N6}L^uP`^rl>t;03iqx>{IKczred|iOJG?4wgZ|At0?#jF24Z&`YzD(`*O$2V zw_HV^3!M?c88teMF|pAzcS8sL%+9!WV$R*R4P*31``Q?%X{k9PWDdA6ou&}q1})|I zYJGmED{zY|rci$P6yD?{jYfAZ(<2qRiL0J8lJ zIUt+3iu5MnXJvJ{8`ZF8xnXhHb@+6a3QPz|XX!bB0k_WQJyG8=}0dO%b9;z)+4!YP%^b^;m0VjRC)f7xQ>%gv{<(sMMqh;0ODbn&r lSSbxUJq#ejG5iSkSgM=*c9Z884^MzUDRFtR5>Y+B{{x51fkXfR From fad5bd263a685521b971a4242721161de2a55478 Mon Sep 17 00:00:00 2001 From: Joshua Attridge Date: Sat, 24 Feb 2024 10:27:15 +0000 Subject: [PATCH 2/8] cleaned up highs_lows data --- smartmoneyconcepts/smc.py | 4 +++- tests/SMC.test_binance.py | 6 +++--- tests/test_binance.png | Bin 46300 -> 65441 bytes 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index a68fbcd..b8b56f9 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -151,8 +151,10 @@ def highs_lows(cls, ohlc: DataFrame) -> Series: else: highs_lows[i + 1] = -1 + highs_lows = np.where(highs_lows != 0, highs_lows, np.nan) + levels = np.where( - highs_lows != 0, + ~np.isnan(highs_lows), np.where(highs_lows == 1, ohlc["high"], ohlc["low"]), np.nan, ) diff --git a/tests/SMC.test_binance.py b/tests/SMC.test_binance.py index 1be3649..1249c9d 100644 --- a/tests/SMC.test_binance.py +++ b/tests/SMC.test_binance.py @@ -76,7 +76,7 @@ def add_highs_lows(fig): indexs = [] levels = [] for i in range(len(highs_lows_data)): - if highs_lows_data["HighsLows"][i] != 0: + if not np.isnan(highs_lows_data["HighsLows"][i]): indexs.append(i) levels.append(highs_lows_data["Levels"][i]) @@ -328,8 +328,8 @@ def add_liquidity(fig): return fig -fig = add_FVG(fig) -# fig = add_highs_lows(fig) +# fig = add_FVG(fig) +fig = add_highs_lows(fig) # fig = add_swing_tops_bottoms(fig) # fig = add_bos_choch(fig) # fig = add_OB(fig) diff --git a/tests/test_binance.png b/tests/test_binance.png index 4503e740b38a9833d472701a337a804585b20a62..461cd9b1539e11beda8b177ebb8334d0e2bb5a03 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swing_tops_bottoms --- smartmoneyconcepts/smc.py | 6 +++++- tests/SMC.test_binance.py | 6 +++--- tests/test_binance.png | Bin 65441 -> 58356 bytes 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index b8b56f9..a78bb7a 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -200,8 +200,12 @@ def swing_tops_bottoms( except IndexError: pass + swing_tops_bottoms = np.where( + swing_tops_bottoms != 0, swing_tops_bottoms, np.nan + ) + levels = np.where( - swing_tops_bottoms != 0, + ~np.isnan(swing_tops_bottoms), np.where(swing_tops_bottoms == 1, ohlc["high"], ohlc["low"]), np.nan, ) diff --git a/tests/SMC.test_binance.py b/tests/SMC.test_binance.py index 1249c9d..8623801 100644 --- a/tests/SMC.test_binance.py +++ b/tests/SMC.test_binance.py @@ -106,7 +106,7 @@ def add_swing_tops_bottoms(fig): indexs = [] levels = [] for i in range(len(swing_tops_bottoms_data)): - if swing_tops_bottoms_data["SwingTopsBottoms"][i] != 0: + if not np.isnan(swing_tops_bottoms_data["SwingTopsBottoms"][i]): indexs.append(i) levels.append(swing_tops_bottoms_data["Levels"][i]) @@ -329,8 +329,8 @@ def add_liquidity(fig): # fig = add_FVG(fig) -fig = add_highs_lows(fig) -# fig = add_swing_tops_bottoms(fig) +# fig = add_highs_lows(fig) +fig = add_swing_tops_bottoms(fig) # fig = add_bos_choch(fig) # fig = add_OB(fig) # fig = add_VOB(fig) diff --git a/tests/test_binance.png b/tests/test_binance.png index 461cd9b1539e11beda8b177ebb8334d0e2bb5a03..126e823a73a9a50202c7a1656177f529c4b04368 100644 GIT binary patch literal 58356 zcmeFZcT`l%_BASkNEFFPR1ry%L_o4g5|AVyxk(~PK#PAAnxDGP%KJBOT-;rU7H4(4(#TvUz0?Q197G%))CPp~@*Z-JyL|Y{GjPkomC`)C@^dMWf9TqWQsRcD~-b%k7%RQvRm!xue&=j(~aCg zZ`b8Q>o_%`vr%`@F=p!M7<9heXH08h#?Nc1EeV;!=Lip0K*y+1!*l~B!6gpU4X>hz zLvptyga7G81g}9@CLZ zQo;Bi$p0R{8aieMT$bwSfmms93lxMh49OkKMFb}SV*jwzxTT?+Q zv^AlY!&*?waxLgAxGYDoC^37s!BpE${*i-`&pFz#~+0*lU6 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zlaL#JC&IM0ACAoTha~?&Gw6Hl_l~x*x8fD4OKJiVQ4+5^Ng*NvPb{$LOE0q=hS|!& zi-DiJPxa+J*8U*XyFkrt3jJjcEqWeal)vIh<-FGL>lzuX765=d#{NtE@8c>-e^Vd1 z0rZPdKkOvEc+f-Or~@oY;8URZYgw6w#x>B7n0&{cHn(k;=dU09WXVXvT)HnZNiZIL z@u;&qFG~Y@Weqb^EMux23}`N9{WQ-Fv5Y3&qx9J2o9&&Q5eoYHzX7ppO%MZOpR?)8 zjq#mMVe_N@ze!X2d%w0#)PbHKCJj+>$Rs=Tn;&J02B2^qmVRE-p4*USD8cnK5oMqp98&VX?Hy z@M92qR|5MQk~rZ3We`&A?H6?%GNp8KA$R=|mDLn1DQq6UYABj<&(7YC$MIBWbHI zZECzpFY&$NzreCRJrML~|XMaTP@??+g~sJ zhMY@!@Y|;Mxrg4XOn^Q56@&La9<$PgGw)*g5BmBq175}*aD|(7JNG1(j_>#e@KTgh KlP!@p{qSFluZ^4l From 901535537b43750d749ff8ae64097b5382136b05 Mon Sep 17 00:00:00 2001 From: Joshua Attridge Date: Mon, 26 Feb 2024 21:50:39 +0000 Subject: [PATCH 4/8] Removed fluff from the BOS CHOCH and cleaned up the returned values --- smartmoneyconcepts/smc.py | 33 +++++++++------------------------ tests/SMC.test_binance.py | 14 +++++++------- tests/test_binance.png | Bin 58356 -> 53574 bytes 3 files changed, 16 insertions(+), 31 deletions(-) diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index a78bb7a..e0821cc 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -216,9 +216,7 @@ def swing_tops_bottoms( return pd.concat([swing_tops_bottoms, levels], axis=1) @classmethod - def bos_choch( - cls, ohlc: DataFrame, close_break=True, filter_liquidity=False - ) -> Series: + def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: """ BOS - Breakout Signal CHoCH - Change of Character signal @@ -230,14 +228,6 @@ def bos_choch( levels = highs_lows["Levels"] highs_lows = highs_lows["HighsLows"] - # filter out the highs and lows used if it is aligned with liquidity - if filter_liquidity: - liquidity = cls.liquidity(ohlc) - liquidity = liquidity["Liquidity"] - for i in range(len(highs_lows)): - if liquidity[i] != 0 and highs_lows[i] != 0: - highs_lows[i] = 0 - levels_order = [] highs_lows_order = [] @@ -248,11 +238,13 @@ def bos_choch( last_positions = [] for i in range(len(highs_lows)): - if highs_lows[i] != 0: + print(i, highs_lows[i]) + if not np.isnan(highs_lows[i]): levels_order.append(levels[i]) highs_lows_order.append(highs_lows[i]) if len(levels_order) >= 4: # bullish bos + print(highs_lows_order[-4:]) bos[last_positions[-2]] = ( 1 if ( @@ -351,18 +343,11 @@ def bos_choch( choch[i] = 0 level[i] = 0 - # there can only be one high or low between the bos/choch and the broken index - for i in np.where(np.logical_or(bos != 0, choch != 0))[0]: - # count the number of highs or lows between the bos/choch and the broken index - count = 0 - for j in range(i, broken[i]): - if highs_lows[j] != 0: - count += 1 - # if there is more than 1 high or low then remove the bos/choch - if count > 2: - bos[i] = 0 - choch[i] = 0 - level[i] = 0 + # replace all the 0s with np.nan + bos = np.where(bos != 0, bos, np.nan) + choch = np.where(choch != 0, choch, np.nan) + level = np.where(level != 0, level, np.nan) + broken = np.where(broken != 0, broken, np.nan) bos = pd.Series(bos, name="BOS") choch = pd.Series(choch, name="CHOCH") diff --git a/tests/SMC.test_binance.py b/tests/SMC.test_binance.py index 8623801..b64a12f 100644 --- a/tests/SMC.test_binance.py +++ b/tests/SMC.test_binance.py @@ -28,7 +28,7 @@ def import_data(symbol, start_str, timeframe): df = import_data("BTCUSDT", "2021-01-01", "1d") -df = df.iloc[-100:] +df = df.iloc[-500:] fig = go.Figure( data=[ @@ -134,10 +134,10 @@ def add_bos_choch(fig): bos_choch_data = smc.bos_choch(df) for i in range(len(bos_choch_data["BOS"])): - if bos_choch_data["BOS"][i] != 0: + if not np.isnan(bos_choch_data["BOS"][i]): fig.add_trace( go.Scatter( - x=[df.index[i], df.index[bos_choch_data["BrokenIndex"][i]]], + x=[df.index[i], df.index[int(bos_choch_data["BrokenIndex"][i])]], y=[bos_choch_data["Level"][i], bos_choch_data["Level"][i]], mode="lines", line=dict( @@ -145,10 +145,10 @@ def add_bos_choch(fig): ), ) ) - if bos_choch_data["CHOCH"][i] != 0: + if not np.isnan(bos_choch_data["CHOCH"][i]): fig.add_trace( go.Scatter( - x=[df.index[bos_choch_data["BrokenIndex"][i]], df.index[i]], + x=[df.index[int(bos_choch_data["BrokenIndex"][i])], df.index[i]], y=[bos_choch_data["Level"][i], bos_choch_data["Level"][i]], mode="lines", line=dict( @@ -330,8 +330,8 @@ def add_liquidity(fig): # fig = add_FVG(fig) # fig = add_highs_lows(fig) -fig = add_swing_tops_bottoms(fig) -# fig = add_bos_choch(fig) +# fig = add_swing_tops_bottoms(fig) +fig = add_bos_choch(fig) # fig = add_OB(fig) # fig = add_VOB(fig) # fig = add_liquidity(fig) diff --git a/tests/test_binance.png b/tests/test_binance.png index 126e823a73a9a50202c7a1656177f529c4b04368..f5f81cd2630ba194a087ce8ec13a30fdbfad2047 100644 GIT binary patch literal 53574 zcmeFZbySq=+cvDCq%=r_f=YLagi0wWAu;5Tf`~ALz|aZ|A!Pzei*!lX05T{bDJTsC z;}Ft=q~v#vzrCMlZ#M3?zVDB3t@n@HH4E>!;>_bb&MRD7Q;nRIne^DPW8^okE8jkL z>;&@IvEysRgy1jZM*HK(j-5SrL;0#69J-K1;;Yv;bP(`7DOf;&B;g8*?|d8Q;A7Hz zFFw|(Jh@kp`uR=jSNg`dz&D2WV^{9dD}Pmgs#0*d|6Q*BK?(11dK>D~=PhrvCf^V{ 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zDhK|6ePXqBkIt}e^gL>$x*_2dgQ4cn#p;O*Chpn=T)vTULHg_|gT!x9XOD0Bz_QeP zPx#uJvP|WDeBfa56)^a=X1Sp>_w_eI!S4IGs&*v&JfpbC{M?Pr^IET~u+fWAn Date: Thu, 29 Feb 2024 18:44:32 +0000 Subject: [PATCH 5/8] cleaned data from ob --- smartmoneyconcepts/smc.py | 10 +++++++++- tests/SMC.test_binance.py | 8 ++++---- tests/test_binance.png | Bin 53574 -> 49825 bytes 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index e0821cc..712cc70 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -367,8 +367,11 @@ def ob(cls, ohlc: DataFrame) -> Series: fvg = cls.fvg(ohlc) ob = np.where( - (fvg["FVG"].shift(-1) != 0) & (fvg["FVG"] == 0), fvg["FVG"].shift(-1), 0 + (fvg["FVG"].shift(-1) != 0) & (~np.isnan(fvg["FVG"])), + fvg["FVG"].shift(-1), + np.nan, ) + # top is equal to the current candles high unless the ob is -1 and the next candles high is higher than the current candles high then top is equal to the next candles high top = np.where( (ob == -1) & (ohlc["high"].shift(-1) > ohlc["high"]), @@ -382,6 +385,9 @@ def ob(cls, ohlc: DataFrame) -> Series: ohlc["low"], ) + top = np.where(np.isnan(ob), np.nan, top) + bottom = np.where(np.isnan(ob), np.nan, bottom) + # set mitigated to np.nan mitigated_index = np.zeros(len(ohlc), dtype=np.int32) for i in np.where(ob != 0)[0]: @@ -394,6 +400,8 @@ def ob(cls, ohlc: DataFrame) -> Series: j = np.argmax(mask) + i + 2 mitigated_index[i] = j + mitigated_index = np.where(np.isnan(ob), np.nan, mitigated_index) + # create a series for each of the keys in the dictionary ob = pd.Series(ob, name="OB") top = pd.Series(top, name="Top") diff --git a/tests/SMC.test_binance.py b/tests/SMC.test_binance.py index b64a12f..f3bdadf 100644 --- a/tests/SMC.test_binance.py +++ b/tests/SMC.test_binance.py @@ -165,8 +165,8 @@ def add_OB(fig): # plot the same way as FVG for i in range(len(ob_data["OB"])): - if ob_data["OB"][i] == 1: - x1 = ( + if not np.isnan(ob_data["OB"][i]): + x1 = int( ob_data["MitigatedIndex"][i] if ob_data["MitigatedIndex"][i] != 0 else len(df) - 1 @@ -331,8 +331,8 @@ def add_liquidity(fig): # fig = add_FVG(fig) # fig = add_highs_lows(fig) # fig = add_swing_tops_bottoms(fig) -fig = add_bos_choch(fig) -# fig = add_OB(fig) +# fig = add_bos_choch(fig) +fig = add_OB(fig) # fig = add_VOB(fig) # fig = add_liquidity(fig) fig.update_layout(xaxis_rangeslider_visible=False) diff --git a/tests/test_binance.png b/tests/test_binance.png index f5f81cd2630ba194a087ce8ec13a30fdbfad2047..3a1d118296114677cbfddc8a5c99307d1696073c 100644 GIT binary patch literal 49825 zcmeFZc{tSV`#+p)AtcEbDoG_GA$uiMLfR(V2u0Sh8|zqZWKRp(L#dD@%UH%T)?`a! ztYeTR+l*}ngL$s$zVFZP&aKbycO1`i9MAC_&-b5>%6QNFx~_A1ov-tCUZFR1G+CMX zn0M{k#d`J1<(s>9?MCg|MYFtz0sPOf{?_oWU59sFy?pUD96CG1Xl0`DKj3m5o#s+zSW`f&+sTH$u&D^>A|9h4xz11da`uT9?5U& zIoA()FYEL+f^Js)!=<^-{7U0uQhx$tSajs2Yi6g30y)-gu)^*tHP&>)hX}$&2Dm|2TC%NaWC5B&|))_Ub5)yuBW)}A5*+R zxbB&&gz2JV@D~dEQ3^+r&oZOM5>Ey|dZNKkCV4|~-FDXq(<{f|t_J&2*94fL$xn2^ 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zuewYUKVdMY0%p=$lkw_d2Q2z;VFXwk9nV^ePsF`n?lQyptI*FpmOZK^=D!vxM9G4 zgJv4~%q%!j*D8|ckX|Co+=5K}wEpgJ+?<16RA7F$XHvFucCwypn5N$gKKl3{v4_{9 zzh<&ce5c4AE56t1Vnimr=P3HZ+dcHi*>j0okEc(_f{zak+2RsIp4{Y`O_VR3*8Bd{ z>i#PiWrpX*fo8ZkiTzVuwdTJptDLV7WTy~xClgW$E9lt&+^oMFN7q5PR;1Uc$YUEc z;m5@R97&H9x^^oP1;h97S=y)-gR<6MRGEi7?D8VPN+x+N9}hNw;yvLsfu=xF*`R6b z)?ks2_ZJ)65ky`A_&(jMdk9SgOL*pNWax!!7_O%B&yDyB#yEpMxTc@Fl*$P=M?#9d zsbe29=Vy1NHoLKWshLl9UUN~MU&?*#X2cF1I>EHm@`2x&Ev%((3Z8l9|zW!yk{=X z;gI0-7Z0n4;~e7Y%r>j-jxJ1E-HOjB>)Ys(D$L?_n`jl|J?heHR)q|$p2)zy8>k%M z;wh}H{3vq#E)>d8;Ck0UMQ2*gI&*bW#jc!P=B~L_gBk%cs2hvTi1+H-^*3)>Uw{F&Nfr0N`QM%r`FU~7`;k#*sJA3vOY3iR;9EGGVQ!@@GkkdJw`DtI%H_<=Dv#_{dt%?vKXE~z~B+ShkV`9E* zIY3jI`m*e0XPI=?eQ{!lzk=Y7O^5JqZ=NjJ*tOqv w5>rH@yE{tts=ot4bVOGljQR;&Rg5d_TQCKT_=^%cH1OxKoSJOuqvx;w4}0=&IRF3v From 1f6c8f0e1e589157f503e2835148aab83742df7c Mon Sep 17 00:00:00 2001 From: Joshua Attridge Date: Thu, 7 Mar 2024 21:56:28 +0000 Subject: [PATCH 6/8] Overall Improvements: Improved returned data removed bad functions made vob default ob function improved graph improved README --- README.md | 106 +++++-- setup.py | 4 +- smartmoneyconcepts/smc.py | 602 ++++++++++++++++++-------------------- tests/SMC.test_binance.py | 133 +++------ tests/test_binance.png | Bin 49825 -> 76399 bytes 5 files changed, 405 insertions(+), 440 deletions(-) diff --git a/README.md b/README.md index b1060eb..b4c0c14 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ The Smart Money Concepts Python Indicator is a sophisticated financial tool developed for traders and investors to gain insights into market sentiment, trends, and potential reversals. This indicator is built using Python, a versatile programming language known for its data analysis and visualization capabilities. +![alt text](https://github.com/joshyattridge/smart-money-concepts/blob/4347d840f625d1334f10713d18213b98da1303fb/tests/test_binance.png) + ## Installation ```bash @@ -20,28 +22,93 @@ smc expects properly formated ohlc DataFrame, with column names in lowercase: [" ## Indicators -- FVG - Fair Value Gap -- Highs and Lows -- Swing Tops and Bottoms -- BOS and CHoCH -- OB - Order Block -- VOB - Volumized Order Blocks -- Liquidity +### Fair Value Gap (FVG) + +```python + smc.fvg(ohlc) +``` + +A fair value gap is when the previous high is lower than the next low if the current candle is bullish. +Or when the previous low is higher than the next high if the current candle is bearish. -## Examples +returns: +FVG = 1 if bullish fair value gap, -1 if bearish fair value gap +Top = the top of the fair value gap +Bottom = the bottom of the fair value gap +MitigatedIndex = the index of the candle that mitigated the fair value gap -Please take a look at smc.test.py for more detailed examples on how each indicator works. +### Swing Highs and Lows ```python - smc.fvg(ohlc) # Fair Value Gap - smc.highs_lows(ohlc) # Highs and Lows - smc.swing_tops_bottoms(ohlc, swing_length=10) # Swing Tops and Bottoms - smc.bos_choch(ohlc, close_break=True, filter_liquidity=False) # Detect BOS and CHoCH - smc.ob(ohlc) # Order Block - smc.vob(ohlc) # Volumized Order Blocks - smc.liquidity(ohlc) # Liquidity + smc.swing_highs_lows(ohlc, swing_length = 50) ``` +A swing high is when the current high is the highest high out of the swing_length amount of candles before and after. +A swing low is when the current low is the lowest low out of the swing_length amount of candles before and after. + +parameters: +swing_length: int - the amount of candles to look back and forward to determine the swing high or low + +returns: +HighLow = 1 if swing high, -1 if swing low +Level = the level of the swing high or low + +### Break of Structure (BOS) & Change of Character (CHoCH) + +```python + smc.bos_choch(ohlc, swing_highs_lows, close_break = True) +``` + +These are both indications of market structure changing + +parameters: +swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function +close_break: bool - if True then the break of structure will be mitigated based on the close of the candle otherwise it will be the high/low. + +returns: +BOS = 1 if bullish break of structure, -1 if bearish break of structure +CHOCH = 1 if bullish change of character, -1 if bearish change of character +Level = the level of the break of structure or change of character +BrokenIndex = the index of the candle that broke the level + +### Order Blocks (OB) + +```python + smc.ob(ohlc, swing_highs_lows, close_mitigation = False) +``` + +This method detects order blocks when there is a high amount of market orders exist on a price range. + +parameters: +swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function +close_mitigation: bool - if True then the order block will be mitigated based on the close of the candle otherwise it will be the high/low. + +returns: +OB = 1 if bullish order block, -1 if bearish order block +Top = top of the order block +Bottom = bottom of the order block +OBVolume = volume + 2 last volumes amounts +Percentage = strength of order block (min(highVolume, lowVolume)/max(highVolume,lowVolume)) + +### Liquidity + +```python + smc.liquidity(ohlc, swing_highs_lows, range_percent = 0.01) +``` + +Liquidity is when there are multiply highs within a small range of each other. +or multiply lows within a small range of each other. + +parameters: +swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function +range_percent: float - the percentage of the range to determine liquidity + +returns: +Liquidity = 1 if bullish liquidity, -1 if bearish liquidity +Level = the level of the liquidity +End = the index of the last liquidity level +Swept = the index of the candle that swept the liquidity + ## Contributing This project is still in BETA so please feel free to contribute to the project. By creating your own indicators or improving the existing ones. @@ -49,7 +116,6 @@ This project is still in BETA so please feel free to contribute to the project. 1. Fork it (https://github.com/joshyattridge/smartmoneyconcepts/fork). 2. Study how it's implemented. 3. Create your feature branch (git checkout -b my-new-feature). -4. Run black code formatter on the finta.py to ensure uniform code style. -5. Commit your changes (git commit -am 'Add some feature'). -6. Push to the branch (git push origin my-new-feature). -7. Create a new Pull Request. +4. Commit your changes (git commit -am 'Add some feature'). +5. Push to the branch (git push origin my-new-feature). +6. Create a new Pull Request. diff --git a/setup.py b/setup.py index e2c9635..9c5bb00 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ import codecs import os -VERSION = '0.0.14' +VERSION = '0.0.15' DESCRIPTION = 'Getting indicators based on smart money concepts or ICT' # read the contents of the README file @@ -18,7 +18,7 @@ long_description_content_type="text/markdown", long_description=LONG_DESCRIPTION, packages=["smartmoneyconcepts"], - install_requires=["pandas==2.0.2", "numpy==1.24.3", "zigzag", "finta==1.3"], + install_requires=["pandas==2.0.2", "numpy==1.24.3"], keywords=['smart', 'money', 'concepts', 'ict', 'indicators', 'trading', 'forex', 'stocks', 'crypto', 'order', 'blocks', 'liquidity'], url="https://github.com/joshyattridge/smartmoneyconcepts", classifiers=[ diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index 712cc70..f52c63c 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -2,8 +2,6 @@ import pandas as pd import numpy as np from pandas import DataFrame, Series -from zigzag import * -from finta import TA def inputvalidator(input_="ohlc"): @@ -52,12 +50,7 @@ def decorate(cls): @apply(inputvalidator(input_="ohlc")) class smc: - __version__ = "0.0.14" - - atr_multiplier = 1.5 - range_percent = 0.01 - swing_length = 10 - close_mitigation = False + __version__ = "0.0.15" @classmethod def fvg(cls, ohlc: DataFrame) -> Series: @@ -65,6 +58,12 @@ def fvg(cls, ohlc: DataFrame) -> Series: FVG - Fair Value Gap A fair value gap is when the previous high is lower than the next low if the current candle is bullish. Or when the previous low is higher than the next high if the current candle is bearish. + + returns: + FVG = 1 if bullish fair value gap, -1 if bearish fair value gap + Top = the top of the fair value gap + Bottom = the bottom of the fair value gap + MitigatedIndex = the index of the candle that mitigated the fair value gap """ fvg = np.where( @@ -81,18 +80,27 @@ def fvg(cls, ohlc: DataFrame) -> Series: ) top = np.where( - ohlc["close"] > ohlc["open"], ohlc["low"].shift(-1), ohlc["low"].shift(1) + ~np.isnan(fvg), + np.where( + ohlc["close"] > ohlc["open"], + ohlc["low"].shift(-1), + ohlc["low"].shift(1), + ), + np.nan, ) + bottom = np.where( - ohlc["close"] > ohlc["open"], ohlc["high"].shift(1), ohlc["high"].shift(-1) + ~np.isnan(fvg), + np.where( + ohlc["close"] > ohlc["open"], + ohlc["high"].shift(1), + ohlc["high"].shift(-1), + ), + np.nan, ) - nan_indices = np.isnan(fvg) - top[nan_indices] = np.nan - bottom[nan_indices] = np.nan - mitigated_index = np.zeros(len(ohlc), dtype=np.int32) - for i in np.where(fvg != 0)[0]: + for i in np.where(~np.isnan(fvg))[0]: mask = np.zeros(len(ohlc), dtype=np.bool_) if fvg[i] == 1: mask = ohlc["low"][i + 2 :] <= top[i] @@ -102,133 +110,122 @@ def fvg(cls, ohlc: DataFrame) -> Series: j = np.argmax(mask) + i + 2 mitigated_index[i] = j - # if fvg is np.nan then set mitigated_index to np.nan mitigated_index = np.where(np.isnan(fvg), np.nan, mitigated_index) - # create a series for each of the keys in the dictionary - fvg = pd.Series(fvg, name="FVG") - top = pd.Series(top, name="Top") - bottom = pd.Series(bottom, name="Bottom") - mitigated_index = pd.Series(mitigated_index, name="MitigatedIndex") - - return pd.concat([fvg, top, bottom, mitigated_index], axis=1) - - @classmethod - def highs_lows(cls, ohlc: DataFrame) -> Series: - pip_range = TA.ATR(ohlc, 14) / ohlc["close"].iloc[-1] * cls.atr_multiplier - pip_range = pip_range.iloc[-1] - - highs_lows = peak_valley_pivots(ohlc["close"], abs(pip_range), -abs(pip_range)) - - still_adjusting = True - while still_adjusting: - still_adjusting = False - for i in range(1, len(highs_lows) - 1): - if highs_lows[i] == 1: - previous_high = ohlc["high"][i - 1] - current_high = ohlc["high"][i] - next_high = ohlc["high"][i + 1] - if (previous_high > current_high and highs_lows[i - 1] == 0) or ( - next_high > current_high and highs_lows[i + 1] == 0 - ): - highs_lows[i] = 0 - still_adjusting = True - if previous_high > next_high and highs_lows[i - 1] == 0: - highs_lows[i - 1] = 1 - else: - highs_lows[i + 1] = 1 - if highs_lows[i] == -1: - previous_low = ohlc["low"][i - 1] - current_low = ohlc["low"][i] - next_low = ohlc["low"][i + 1] - if (previous_low < current_low and highs_lows[i - 1] == 0) or ( - next_low < current_low and highs_lows[i + 1] == 0 - ): - highs_lows[i] = 0 - still_adjusting = True - if previous_low < next_low and highs_lows[i - 1] == 0: - highs_lows[i - 1] = -1 - else: - highs_lows[i + 1] = -1 - - highs_lows = np.where(highs_lows != 0, highs_lows, np.nan) - - levels = np.where( - ~np.isnan(highs_lows), - np.where(highs_lows == 1, ohlc["high"], ohlc["low"]), - np.nan, + return pd.concat( + [ + pd.Series(fvg, name="FVG"), + pd.Series(top, name="Top"), + pd.Series(bottom, name="Bottom"), + pd.Series(mitigated_index, name="MitigatedIndex"), + ], + axis=1, ) - highs_lows = pd.Series(highs_lows, name="HighsLows") - levels = pd.Series(levels, name="Levels") - - return pd.concat([highs_lows, levels], axis=1) - @classmethod - def swing_tops_bottoms( - cls, ohlc: DataFrame, swing_length: int = swing_length + def swing_highs_lows( + cls, ohlc: DataFrame, swing_length: int = 50 ) -> Series: - swing_tops_bottoms = np.zeros(len(ohlc), dtype=np.int32) - swing_type = 0 - prev_swing_type = 0 + """ + Swing Highs and Lows + A swing high is when the current high is the highest high out of the swing_length amount of candles before and after. + A swing low is when the current low is the lowest low out of the swing_length amount of candles before and after. - # Calculate the highest high and lowest low for the specified length - upper = ohlc["high"].rolling(window=swing_length).max() - lower = ohlc["low"].rolling(window=swing_length).min() + parameters: + swing_length: int - the amount of candles to look back and forward to determine the swing high or low - # Concatenate upper and lower to df - ohlc["upper"] = upper - ohlc["lower"] = lower + returns: + HighLow = 1 if swing high, -1 if swing low + Level = the level of the swing high or low + """ - # Iterate over each index in the dataframe - for i in range(len(ohlc)): - try: - # Determine the swing type - if ohlc["high"].iloc[i] > upper.iloc[i + swing_length]: - swing_type = 0 - elif ohlc["low"].iloc[i] < lower.iloc[i + swing_length]: - swing_type = 1 - - # Check if it's a new top or bottom - if swing_type == 0 and prev_swing_type != 0: - swing_tops_bottoms[i] = 1 - elif swing_type == 1 and prev_swing_type != 1: - swing_tops_bottoms[i] = -1 - - # Update the previous swing type - prev_swing_type = swing_type - except IndexError: - pass - - swing_tops_bottoms = np.where( - swing_tops_bottoms != 0, swing_tops_bottoms, np.nan + swing_length *= 2 + # set the highs to 1 if the current high is the highest high in the last 5 candles and next 5 candles + swing_highs_lows = np.where( + ohlc["high"] + == ohlc["high"] + .shift(-(swing_length // 2)) + .rolling(swing_length) + .max(), + 1, + np.where( + ohlc["low"] + == ohlc["low"] + .shift(-(swing_length // 2)) + .rolling(swing_length) + .min(), + -1, + np.nan, + ), ) - levels = np.where( - ~np.isnan(swing_tops_bottoms), - np.where(swing_tops_bottoms == 1, ohlc["high"], ohlc["low"]), + continue_ = True + while continue_: + positions = np.where(~np.isnan(swing_highs_lows))[0] + continue_ = False + for i in range(len(positions) - 1): + current, next = ( + swing_highs_lows[positions[i]], + swing_highs_lows[positions[i + 1]], + ) + high, low = ( + ohlc["high"].iloc[positions[i]], + ohlc["low"].iloc[positions[i]], + ) + next_high, next_low = ( + ohlc["high"].iloc[positions[i + 1]], + ohlc["low"].iloc[positions[i + 1]], + ) + if current == -1 and next == -1: + remove_index = positions[i] if low > next_low else positions[i + 1] + swing_highs_lows[remove_index] = np.nan + continue_ = True + elif current == 1 and next == 1: + remove_index = ( + positions[i] if high < next_high else positions[i + 1] + ) + swing_highs_lows[remove_index] = np.nan + continue_ = True + + positions = np.where(~np.isnan(swing_highs_lows))[0] + if swing_highs_lows[positions[0]] == 1: + swing_highs_lows[0] = -1 + if swing_highs_lows[positions[-1]] == -1: + swing_highs_lows[-1] = 1 + + level = np.where( + ~np.isnan(swing_highs_lows), + np.where(swing_highs_lows == 1, ohlc["high"], ohlc["low"]), np.nan, ) - swing_tops_bottoms = pd.Series(swing_tops_bottoms, name="SwingTopsBottoms") - levels = pd.Series(levels, name="Levels") - - return pd.concat([swing_tops_bottoms, levels], axis=1) + return pd.concat( + [ + pd.Series(swing_highs_lows, name="HighLow"), + pd.Series(level, name="Level"), + ], + axis=1, + ) @classmethod - def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: + def bos_choch(cls, ohlc: DataFrame, swing_highs_lows: DataFrame, close_break:bool = True) -> Series: """ - BOS - Breakout Signal - CHoCH - Change of Character signal - This is when the current candle is the first candle to break out of a range. + BOS - Break of Structure + CHoCH - Change of Character + these are both indications of market structure changing + + parameters: + swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function + close_break: bool - if True then the break of structure will be mitigated based on the close of the candle otherwise it will be the high/low. + + returns: + BOS = 1 if bullish break of structure, -1 if bearish break of structure + CHOCH = 1 if bullish change of character, -1 if bearish change of character + Level = the level of the break of structure or change of character + BrokenIndex = the index of the candle that broke the level """ - # get the highs and lows - highs_lows = cls.highs_lows(ohlc) - levels = highs_lows["Levels"] - highs_lows = highs_lows["HighsLows"] - - levels_order = [] + level_order = [] highs_lows_order = [] bos = np.zeros(len(ohlc), dtype=np.int32) @@ -237,29 +234,27 @@ def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: last_positions = [] - for i in range(len(highs_lows)): - print(i, highs_lows[i]) - if not np.isnan(highs_lows[i]): - levels_order.append(levels[i]) - highs_lows_order.append(highs_lows[i]) - if len(levels_order) >= 4: + for i in range(len(swing_highs_lows["HighLow"])): + if not np.isnan(swing_highs_lows["HighLow"][i]): + level_order.append(swing_highs_lows["Level"][i]) + highs_lows_order.append(swing_highs_lows["HighLow"][i]) + if len(level_order) >= 4: # bullish bos - print(highs_lows_order[-4:]) bos[last_positions[-2]] = ( 1 if ( np.all(highs_lows_order[-4:] == [-1, 1, -1, 1]) and np.all( - levels_order[-4] - < levels_order[-2] - < levels_order[-3] - < levels_order[-1] + level_order[-4] + < level_order[-2] + < level_order[-3] + < level_order[-1] ) ) else 0 ) level[last_positions[-2]] = ( - levels_order[-3] if bos[last_positions[-2]] != 0 else 0 + level_order[-3] if bos[last_positions[-2]] != 0 else 0 ) # bearish bos @@ -268,16 +263,16 @@ def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: if ( np.all(highs_lows_order[-4:] == [1, -1, 1, -1]) and np.all( - levels_order[-4] - > levels_order[-2] - > levels_order[-3] - > levels_order[-1] + level_order[-4] + > level_order[-2] + > level_order[-3] + > level_order[-1] ) ) else bos[last_positions[-2]] ) level[last_positions[-2]] = ( - levels_order[-3] if bos[last_positions[-2]] != 0 else 0 + level_order[-3] if bos[last_positions[-2]] != 0 else 0 ) # bullish choch @@ -286,16 +281,16 @@ def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: if ( np.all(highs_lows_order[-4:] == [-1, 1, -1, 1]) and np.all( - levels_order[-1] - > levels_order[-3] - > levels_order[-4] - > levels_order[-2] + level_order[-1] + > level_order[-3] + > level_order[-4] + > level_order[-2] ) ) else 0 ) level[last_positions[-2]] = ( - levels_order[-3] + level_order[-3] if choch[last_positions[-2]] != 0 else level[last_positions[-2]] ) @@ -306,16 +301,16 @@ def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: if ( np.all(highs_lows_order[-4:] == [1, -1, 1, -1]) and np.all( - levels_order[-1] - < levels_order[-3] - < levels_order[-4] - < levels_order[-2] + level_order[-1] + < level_order[-3] + < level_order[-4] + < level_order[-2] ) ) else choch[last_positions[-2]] ) level[last_positions[-2]] = ( - levels_order[-3] + level_order[-3] if choch[last_positions[-2]] != 0 else level[last_positions[-2]] ) @@ -357,151 +352,24 @@ def bos_choch(cls, ohlc: DataFrame, close_break=True) -> Series: return pd.concat([bos, choch, level, broken], axis=1) @classmethod - def ob(cls, ohlc: DataFrame) -> Series: - """ - OB - Order Block - This is the last candle before a FVG - """ - - # get the FVG - fvg = cls.fvg(ohlc) - - ob = np.where( - (fvg["FVG"].shift(-1) != 0) & (~np.isnan(fvg["FVG"])), - fvg["FVG"].shift(-1), - np.nan, - ) - - # top is equal to the current candles high unless the ob is -1 and the next candles high is higher than the current candles high then top is equal to the next candles high - top = np.where( - (ob == -1) & (ohlc["high"].shift(-1) > ohlc["high"]), - ohlc["high"].shift(-1), - ohlc["high"], - ) - # bottom is equal to the current candles low unless the ob is 1 and the next candles low is lower than the current candles low then bottom is equal to the next candles low - bottom = np.where( - (ob == 1) & (ohlc["low"].shift(-1) < ohlc["low"]), - ohlc["low"].shift(-1), - ohlc["low"], - ) - - top = np.where(np.isnan(ob), np.nan, top) - bottom = np.where(np.isnan(ob), np.nan, bottom) - - # set mitigated to np.nan - mitigated_index = np.zeros(len(ohlc), dtype=np.int32) - for i in np.where(ob != 0)[0]: - mask = np.zeros(len(ohlc), dtype=np.bool_) - if ob[i] == 1: - mask = ohlc["low"][i + 2 :] <= top[i] - elif ob[i] == -1: - mask = ohlc["high"][i + 2 :] >= bottom[i] - if np.any(mask): - j = np.argmax(mask) + i + 2 - mitigated_index[i] = j - - mitigated_index = np.where(np.isnan(ob), np.nan, mitigated_index) - - # create a series for each of the keys in the dictionary - ob = pd.Series(ob, name="OB") - top = pd.Series(top, name="Top") - bottom = pd.Series(bottom, name="Bottom") - mitigated_index = pd.Series(mitigated_index, name="MitigatedIndex") - - return pd.concat([ob, top, bottom, mitigated_index], axis=1) - - @classmethod - def liquidity(cls, ohlc: DataFrame) -> Series: - """ - Liquidity - Liquidity is when there are multiply highs within a small range of each other. - or multiply lows within a small range of each other. - """ - - # subtract the highest high from the lowest low - pip_range = (max(ohlc["high"]) - min(ohlc["low"])) * cls.range_percent - - # get the highs and lows - highs_lows = cls.highs_lows(ohlc) - levels = highs_lows["Levels"] - highs_lows = highs_lows["HighsLows"] - - # go through all of the high levels and if there are more than 1 within the pip range, then it is liquidity - liquidity = np.zeros(len(ohlc), dtype=np.int32) - liquidity_level = np.zeros(len(ohlc), dtype=np.float32) - liquidity_end = np.zeros(len(ohlc), dtype=np.int32) - liquidity_swept = np.zeros(len(ohlc), dtype=np.int32) - - for i in range(len(ohlc)): - if highs_lows[i] == 1: - high_level = levels[i] - range_low = high_level - pip_range - range_high = high_level + pip_range - temp_liquidity_levels = [high_level] - start = i - end = i - swept = 0 - for c in range(i + 1, len(ohlc)): - if highs_lows[c] == 1 and range_low <= levels[c] <= range_high: - end = c - temp_liquidity_levels.append(levels[c]) - highs_lows.loc[c] = 0 - if ohlc["high"][c] >= range_high: - swept = c - break - if len(temp_liquidity_levels) > 1: - average_high = sum(temp_liquidity_levels) / len( - temp_liquidity_levels - ) - liquidity[i] = 1 - liquidity_level[i] = average_high - liquidity_end[i] = end - liquidity_swept[i] = swept - - # now do the same for the lows - for i in range(len(ohlc)): - if highs_lows[i] == -1: - low_level = levels[i] - range_low = low_level - pip_range - range_high = low_level + pip_range - temp_liquidity_levels = [low_level] - start = i - end = i - swept = 0 - for c in range(i + 1, len(ohlc)): - if highs_lows[c] == -1 and range_low <= levels[c] <= range_high: - end = c - temp_liquidity_levels.append(levels[c]) - highs_lows.loc[c] = 0 - if ohlc["low"][c] <= range_low: - swept = c - break - if len(temp_liquidity_levels) > 1: - average_low = sum(temp_liquidity_levels) / len( - temp_liquidity_levels - ) - liquidity[i] = -1 - liquidity_level[i] = average_low - liquidity_end[i] = end - liquidity_swept[i] = swept - - liquidity = pd.Series(liquidity, name="Liquidity") - level = pd.Series(liquidity_level, name="Level") - liquidity_end = pd.Series(liquidity_end, name="End") - liquidity_swept = pd.Series(liquidity_swept, name="Swept") - - return pd.concat([liquidity, level, liquidity_end, liquidity_swept], axis=1) - - @classmethod - def vob( + def ob( cls, - ohlc: pd.DataFrame, - swing_length: int = swing_length, - close_mitigation: bool = close_mitigation, + ohlc: DataFrame, + swing_highs_lows: DataFrame, + close_mitigation:bool = False, ) -> DataFrame: """ - VOB - Volumized Order Blocks + OB - Order Blocks This method detects order blocks when there is a high amount of market orders exist on a price range. + + parameters: + swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function + close_mitigation: bool - if True then the order block will be mitigated based on the close of the candle otherwise it will be the high/low. + + returns: + OB = 1 if bullish order block, -1 if bearish order block + Top = top of the order block + Bottom = bottom of the order block OBVolume = volume + 2 last volumes amounts Percentage = strength of order block (min(highVolume, lowVolume)/max(highVolume,lowVolume)) """ @@ -517,9 +385,6 @@ def vob( mitigated_index = np.zeros(len(ohlc), dtype=np.int32) breaker = np.full(len(ohlc), False, dtype=bool) - ob_swing = cls.swing_tops_bottoms(ohlc, swing_length) - ob_swing = ob_swing["SwingTopsBottoms"] - for i in range(len(ohlc)): close_index = i close_price = ohlc["close"].iloc[close_index] @@ -529,8 +394,15 @@ def vob( for j in range(len(ob) - 1, -1, -1): if ob[j] == 1: currentOB = j - if not breaker[currentOB]: - if ( + if breaker[currentOB]: + if ohlc.high.iloc[close_index] > top[currentOB]: + ob[j] = top[j] = bottom[j] = obVolume[j] = lowVolume[ + j + ] = highVolume[j] = mitigated_index[j] = percentage[ + j + ] = 0.0 + + elif ( not close_mitigation and ohlc.low.iloc[close_index] < bottom[currentOB] ) or ( @@ -541,19 +413,11 @@ def vob( ) < bottom[currentOB] ): - breaker[currentOB] = True - mitigated_index[currentOB] = close_index - 1 - else: - if ohlc.high.iloc[close_index] > top[currentOB]: - ob[j] = top[j] = bottom[j] = obVolume[j] = lowVolume[ - j - ] = highVolume[j] = mitigated_index[j] = percentage[ - j - ] = 0.0 - + breaker[currentOB] = True + mitigated_index[currentOB] = close_index - 1 last_top_index = None - for j in range(len(ob_swing)): - if ob_swing[j] == 1 and j < close_index: + for j in range(len(swing_highs_lows["HighLow"])): + if swing_highs_lows["HighLow"][j] == 1 and j < close_index: last_top_index = j if last_top_index is not None: swing_top_price = ohlc["high"].iloc[last_top_index] @@ -602,8 +466,15 @@ def vob( for j in range(len(ob) - 1, -1, -1): if ob[j] == -1: currentOB = j - if not breaker[currentOB]: - if ( + if breaker[currentOB]: + if ohlc.low.iloc[close_index] < bottom[currentOB]: + ob[j] = top[j] = bottom[j] = obVolume[j] = lowVolume[ + j + ] = highVolume[j] = mitigated_index[j] = percentage[ + j + ] = 0.0 + + elif ( not close_mitigation and ohlc.high.iloc[close_index] > top[currentOB] ) or ( @@ -614,19 +485,11 @@ def vob( ) > top[currentOB] ): - breaker[currentOB] = True - mitigated_index[currentOB] = close_index - else: - if ohlc.low.iloc[close_index] < bottom[currentOB]: - ob[j] = top[j] = bottom[j] = obVolume[j] = lowVolume[ - j - ] = highVolume[j] = mitigated_index[j] = percentage[ - j - ] = 0.0 - + breaker[currentOB] = True + mitigated_index[currentOB] = close_index last_btm_index = None - for j in range(len(ob_swing)): - if ob_swing[j] == -1 and j < close_index: + for j in range(len(swing_highs_lows["HighLow"])): + if swing_highs_lows["HighLow"][j] == -1 and j < close_index: last_btm_index = j if last_btm_index is not None: swing_btm_price = ohlc["low"].iloc[last_btm_index] @@ -666,6 +529,13 @@ def vob( / np.max([highVolume[obIndex], lowVolume[obIndex]], axis=0) ) * 100.0 + ob = np.where(ob != 0, ob, np.nan) + top = np.where(~np.isnan(ob), top, np.nan) + bottom = np.where(~np.isnan(ob), bottom, np.nan) + obVolume = np.where(~np.isnan(ob), obVolume, np.nan) + mitigated_index = np.where(~np.isnan(ob), mitigated_index, np.nan) + percentage = np.where(~np.isnan(ob), percentage, np.nan) + ob_series = pd.Series(ob, name="OB") top_series = pd.Series(top, name="Top") bottom_series = pd.Series(bottom, name="Bottom") @@ -684,3 +554,91 @@ def vob( ], axis=1, ) + + @classmethod + def liquidity(cls, ohlc: DataFrame, swing_highs_lows: DataFrame, range_percent:float = 0.01) -> Series: + """ + Liquidity + Liquidity is when there are multiply highs within a small range of each other. + or multiply lows within a small range of each other. + + parameters: + swing_highs_lows: DataFrame - provide the dataframe from the swing_highs_lows function + range_percent: float - the percentage of the range to determine liquidity + + returns: + Liquidity = 1 if bullish liquidity, -1 if bearish liquidity + Level = the level of the liquidity + End = the index of the last liquidity level + Swept = the index of the candle that swept the liquidity + """ + + # subtract the highest high from the lowest low + pip_range = (max(ohlc["high"]) - min(ohlc["low"])) * range_percent + + # go through all of the high level and if there are more than 1 within the pip range, then it is liquidity + liquidity = np.zeros(len(ohlc), dtype=np.int32) + liquidity_level = np.zeros(len(ohlc), dtype=np.float32) + liquidity_end = np.zeros(len(ohlc), dtype=np.int32) + liquidity_swept = np.zeros(len(ohlc), dtype=np.int32) + + for i in range(len(ohlc)): + if swing_highs_lows["HighLow"][i] == 1: + high_level = swing_highs_lows["Level"][i] + range_low = high_level - pip_range + range_high = high_level + pip_range + temp_liquidity_level = [high_level] + start = i + end = i + swept = 0 + for c in range(i + 1, len(ohlc)): + if swing_highs_lows["HighLow"][c] == 1 and range_low <= swing_highs_lows["Level"][c] <= range_high: + end = c + temp_liquidity_level.append(swing_highs_lows["Level"][c]) + swing_highs_lows.loc[c, "HighLow"] = 0 + if ohlc["high"].iloc[c] >= range_high: + swept = c + break + if len(temp_liquidity_level) > 1: + average_high = sum(temp_liquidity_level) / len(temp_liquidity_level) + liquidity[i] = 1 + liquidity_level[i] = average_high + liquidity_end[i] = end + liquidity_swept[i] = swept + + # now do the same for the lows + for i in range(len(ohlc)): + if swing_highs_lows["HighLow"][i] == -1: + low_level = swing_highs_lows["Level"][i] + range_low = low_level - pip_range + range_high = low_level + pip_range + temp_liquidity_level = [low_level] + start = i + end = i + swept = 0 + for c in range(i + 1, len(ohlc)): + if swing_highs_lows["HighLow"][c] == -1 and range_low <= swing_highs_lows["Level"][c] <= range_high: + end = c + temp_liquidity_level.append(swing_highs_lows["Level"][c]) + swing_highs_lows.loc[c, "HighLow"] = 0 + if ohlc["low"].iloc[c] <= range_low: + swept = c + break + if len(temp_liquidity_level) > 1: + average_low = sum(temp_liquidity_level) / len(temp_liquidity_level) + liquidity[i] = -1 + liquidity_level[i] = average_low + liquidity_end[i] = end + liquidity_swept[i] = swept + + liquidity = np.where(liquidity != 0, liquidity, np.nan) + liquidity_level = np.where(~np.isnan(liquidity), liquidity_level, np.nan) + liquidity_end = np.where(~np.isnan(liquidity), liquidity_end, np.nan) + liquidity_swept = np.where(~np.isnan(liquidity), liquidity_swept, np.nan) + + liquidity = pd.Series(liquidity, name="Liquidity") + level = pd.Series(liquidity_level, name="Level") + liquidity_end = pd.Series(liquidity_end, name="End") + liquidity_swept = pd.Series(liquidity_swept, name="Swept") + + return pd.concat([liquidity, level, liquidity_end, liquidity_swept], axis=1) diff --git a/tests/SMC.test_binance.py b/tests/SMC.test_binance.py index f3bdadf..f047736 100644 --- a/tests/SMC.test_binance.py +++ b/tests/SMC.test_binance.py @@ -23,12 +23,12 @@ def import_data(symbol, start_str, timeframe): df = df.iloc[:, :6] df.columns = ["timestamp", "open", "high", "low", "close", "volume"] df = df.set_index("timestamp") - df.index = pd.to_datetime(df.index, unit="ms") + df.index = pd.to_datetime(df.index, unit="ms").strftime("%Y-%m-%d %H:%M:%S") return df df = import_data("BTCUSDT", "2021-01-01", "1d") -df = df.iloc[-500:] +df = df.iloc[-1000:] fig = go.Figure( data=[ @@ -43,9 +43,7 @@ def import_data(symbol, start_str, timeframe): ) -def add_FVG(fig): - start = time.time() - fvg_data = smc.fvg(df) +def add_FVG(fig, fvg_data): for i in range(len(fvg_data["FVG"])): if not np.isnan(fvg_data["FVG"][i]): x1 = int( @@ -69,58 +67,25 @@ def add_FVG(fig): return fig -def add_highs_lows(fig): - highs_lows_data = smc.highs_lows(df) - - # remove from highs_lows_data - indexs = [] - levels = [] - for i in range(len(highs_lows_data)): - if not np.isnan(highs_lows_data["HighsLows"][i]): - indexs.append(i) - levels.append(highs_lows_data["Levels"][i]) - - # plot these lines on a graph - for i in range(len(indexs) - 1): - fig.add_trace( - go.Scatter( - x=[df.index[indexs[i]], df.index[indexs[i + 1]]], - y=[levels[i], levels[i + 1]], - mode="lines", - line=dict( - color=( - "green" - if highs_lows_data["HighsLows"][indexs[i]] == -1 - else "red" - ), - ), - ) - ) - - return fig - - -def add_swing_tops_bottoms(fig): - swing_tops_bottoms_data = smc.swing_tops_bottoms(df) - +def add_swing_highs_lows(fig, swing_highs_lows_data): indexs = [] - levels = [] - for i in range(len(swing_tops_bottoms_data)): - if not np.isnan(swing_tops_bottoms_data["SwingTopsBottoms"][i]): + level = [] + for i in range(len(swing_highs_lows_data)): + if not np.isnan(swing_highs_lows_data["HighLow"][i]): indexs.append(i) - levels.append(swing_tops_bottoms_data["Levels"][i]) + level.append(swing_highs_lows_data["Level"][i]) # plot these lines on a graph for i in range(len(indexs) - 1): fig.add_trace( go.Scatter( x=[df.index[indexs[i]], df.index[indexs[i + 1]]], - y=[levels[i], levels[i + 1]], + y=[level[i], level[i + 1]], mode="lines", line=dict( color=( "green" - if swing_tops_bottoms_data["SwingTopsBottoms"][indexs[i]] == -1 + if swing_highs_lows_data["HighLow"][indexs[i]] == -1 else "red" ), ), @@ -130,9 +95,7 @@ def add_swing_tops_bottoms(fig): return fig -def add_bos_choch(fig): - bos_choch_data = smc.bos_choch(df) - +def add_bos_choch(fig, bos_choch_data): for i in range(len(bos_choch_data["BOS"])): if not np.isnan(bos_choch_data["BOS"][i]): fig.add_trace( @@ -160,35 +123,7 @@ def add_bos_choch(fig): return fig -def add_OB(fig): - ob_data = smc.ob(df) - - # plot the same way as FVG - for i in range(len(ob_data["OB"])): - if not np.isnan(ob_data["OB"][i]): - x1 = int( - ob_data["MitigatedIndex"][i] - if ob_data["MitigatedIndex"][i] != 0 - else len(df) - 1 - ) - fig.add_shape( - type="rect", - x0=df.index[i], - y0=ob_data["Top"][i], - x1=df.index[x1], - y1=ob_data["Bottom"][i], - line=dict( - width=0, - ), - fillcolor="purple", - opacity=0.5, - ) - return fig - - -def add_VOB(fig): - ob_data = smc.vob(df) - +def add_OB(fig, ob_data): def format_volume(volume): if volume >= 1e12: return f"{volume / 1e12:.3f}T" @@ -203,7 +138,7 @@ def format_volume(volume): for i in range(len(ob_data["OB"])): if ob_data["OB"][i] == 1: - x1 = ( + x1 = int( ob_data["MitigatedIndex"][i] if ob_data["MitigatedIndex"][i] != 0 else len(df) - 1 @@ -245,7 +180,7 @@ def format_volume(volume): for i in range(len(ob_data["OB"])): if ob_data["OB"][i] == -1: - x1 = ( + x1 = int( ob_data["MitigatedIndex"][i] if ob_data["MitigatedIndex"][i] != 0 else len(df) - 1 @@ -287,15 +222,13 @@ def format_volume(volume): return fig -def add_liquidity(fig): - liquidity_data = smc.liquidity(df) - +def add_liquidity(fig, liquidity_data): # draw a line horizontally for each liquidity level for i in range(len(liquidity_data["Liquidity"])): - if liquidity_data["Liquidity"][i] != 0: + if not np.isnan(liquidity_data["Liquidity"][i]): fig.add_trace( go.Scatter( - x=[df.index[i], df.index[liquidity_data["End"][i]]], + x=[df.index[i], df.index[int(liquidity_data["End"][i])]], y=[liquidity_data["Level"][i], liquidity_data["Level"][i]], mode="lines", line=dict( @@ -303,20 +236,20 @@ def add_liquidity(fig): ), ) ) - if liquidity_data["Swept"][i] != 0: + if liquidity_data["Swept"][i] != 0 and not np.isnan(liquidity_data["Swept"][i]): # draw a red line between the end and the swept point fig.add_trace( go.Scatter( x=[ - df.index[liquidity_data["End"][i]], - df.index[liquidity_data["Swept"][i]], + df.index[int(liquidity_data["End"][i])], + df.index[int(liquidity_data["Swept"][i])], ], y=[ liquidity_data["Level"][i], ( - df["high"][liquidity_data["Swept"][i]] + df["high"].iloc[int(liquidity_data["Swept"][i])] if liquidity_data["Liquidity"][i] == 1 - else df["low"][liquidity_data["Swept"][i]] + else df["low"].iloc[int(liquidity_data["Swept"][i])] ), ], mode="lines", @@ -327,15 +260,23 @@ def add_liquidity(fig): ) return fig +fvg_data = smc.fvg(df) +swing_highs_lows_data = smc.swing_highs_lows(df, swing_length=50) +bos_choch_data = smc.bos_choch(df, swing_highs_lows_data) +ob_data = smc.ob(df, swing_highs_lows_data) +liquidity_data = smc.liquidity(df, swing_highs_lows_data) +fig = add_FVG(fig, fvg_data) +fig = add_swing_highs_lows(fig, swing_highs_lows_data) +fig = add_bos_choch(fig, bos_choch_data) +fig = add_OB(fig, ob_data) +fig = add_liquidity(fig, liquidity_data) -# fig = add_FVG(fig) -# fig = add_highs_lows(fig) -# fig = add_swing_tops_bottoms(fig) -# fig = add_bos_choch(fig) -fig = add_OB(fig) -# fig = add_VOB(fig) -# fig = add_liquidity(fig) fig.update_layout(xaxis_rangeslider_visible=False) fig.update_layout(showlegend=False) fig.update_layout(margin=dict(l=0, r=0, b=0, t=0)) +fig.update_xaxes(visible=False, showticklabels=False) +fig.update_yaxes(visible=False, showticklabels=False) +fig.update_layout(plot_bgcolor="rgba(0,0,0,0)") +fig.update_layout(paper_bgcolor="rgba(94, 94, 134, 1)") +fig.update_layout(font=dict(color="white")) fig.write_image("test_binance.png") diff --git a/tests/test_binance.png b/tests/test_binance.png index 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zZv;Ei!&&q6bJ7Ag!|%Rzccc$jNy;R^jg67RyfPJ`utvS=z~?U$velz^#kBt5ZZm$c zsy3*B_#nTLSTsW&ZnH~S%F)nFa5A?NIQPoq_Q_4I2;-*Ol2=mUV9a)!dPLV0eAStD4F-X$XUm^rQ75htu6<{ z02g)}?y4s)+P=@Y$R!}X+*Ar3xyt>l%-BE#xpGAI`*VKYfY%!5b+zUBs z?cen%;fM9BAPYIdDy~Q+jl1A%B9Ezzww^^`{s$b@*v{4xRTBW9X5be zp6+{}Sn)Uw5in?+b;4Pua$zXDL&R|Vt8G_(PP5=BiMn?GgU^hs*H+z%s}jt&RFT+S zIS$kTa94X$V|+j=3Q3$cnbDPh`zEh`s%xN;OTRQs2{!Y>6X(LI{K$!3d6g$*vFG(L z>Nk=jTR_t!If!bKv~-8O`bOv8^GK+L;Br_5G)7yXc;{ z%Js2|wcEzzrE%^J)4V7~2a97VFvR|{eZjTXPCv!jy`!7!Soc7=B`cqx%qQ6yfS Date: Thu, 7 Mar 2024 21:58:22 +0000 Subject: [PATCH 7/8] fixed tabs --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index b4c0c14..c6ae79d 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ smc expects properly formated ohlc DataFrame, with column names in lowercase: [" ### Fair Value Gap (FVG) ```python - smc.fvg(ohlc) +smc.fvg(ohlc) ``` A fair value gap is when the previous high is lower than the next low if the current candle is bullish. @@ -40,7 +40,7 @@ MitigatedIndex = the index of the candle that mitigated the fair value gap ### Swing Highs and Lows ```python - smc.swing_highs_lows(ohlc, swing_length = 50) +smc.swing_highs_lows(ohlc, swing_length = 50) ``` A swing high is when the current high is the highest high out of the swing_length amount of candles before and after. @@ -56,7 +56,7 @@ Level = the level of the swing high or low ### Break of Structure (BOS) & Change of Character (CHoCH) ```python - smc.bos_choch(ohlc, swing_highs_lows, close_break = True) +smc.bos_choch(ohlc, swing_highs_lows, close_break = True) ``` These are both indications of market structure changing @@ -74,7 +74,7 @@ BrokenIndex = the index of the candle that broke the level ### Order Blocks (OB) ```python - smc.ob(ohlc, swing_highs_lows, close_mitigation = False) +smc.ob(ohlc, swing_highs_lows, close_mitigation = False) ``` This method detects order blocks when there is a high amount of market orders exist on a price range. @@ -93,7 +93,7 @@ Percentage = strength of order block (min(highVolume, lowVolume)/max(highVolume, ### Liquidity ```python - smc.liquidity(ohlc, swing_highs_lows, range_percent = 0.01) +smc.liquidity(ohlc, swing_highs_lows, range_percent = 0.01) ``` Liquidity is when there are multiply highs within a small range of each other. From 7437cc9d2237c4ead92e8ec152ec8b8867af9e8d Mon Sep 17 00:00:00 2001 From: Joshua Attridge Date: Thu, 7 Mar 2024 21:59:51 +0000 Subject: [PATCH 8/8] changed image --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c6ae79d..45790bc 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ The Smart Money Concepts Python Indicator is a sophisticated financial tool developed for traders and investors to gain insights into market sentiment, trends, and potential reversals. This indicator is built using Python, a versatile programming language known for its data analysis and visualization capabilities. -![alt text](https://github.com/joshyattridge/smart-money-concepts/blob/4347d840f625d1334f10713d18213b98da1303fb/tests/test_binance.png) +![alt text](https://github.com/joshyattridge/smart-money-concepts/blob/21656dd807c4077f345b6cbf29b1bc37672628e9/tests/test_binance.png) ## Installation