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- .gitattributes +46 -0
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z.Others/z.Images[[:space:]]Folder/Screenshot_20251121_083916_Sheets.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1CPMOTS.rp1_[[:space:]]Is[[:space:]]Treating[[:space:]]Trading[[:space:]]Worse[[:space:]]Than[[:space:]]a[[:space:]]Coin[[:space:]]Flip,[[:space:]]but[[:space:]]with[[:space:]]a[[:space:]]Slight[[:space:]]Edge,[[:space:]]Profitable_/1CPMOTS_rp1.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1CPMOTS.rp1_[[:space:]]Is[[:space:]]Treating[[:space:]]Trading[[:space:]]Worse[[:space:]]Than[[:space:]]a[[:space:]]Coin[[:space:]]Flip,[[:space:]]but[[:space:]]with[[:space:]]a[[:space:]]Slight[[:space:]]Edge,[[:space:]]Profitable_/newplot[[:space:]](2).png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/The[[:space:]]1HRMRSS.rp1.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1SSHSHFT.rp1_[[:space:]][[:space:]]Is[[:space:]]Simultaneous[[:space:]]Stopout[[:space:]]Hedging[[:space:]]Strategy,[[:space:]][[:space:]]a[[:space:]]High[[:space:]]Frequency[[:space:]]Trading,[[:space:]]an[[:space:]]Edge[[:space:]]in[[:space:]]Retail[[:space:]]Level[[:space:]]Application_/The[[:space:]]1SSHSHFT_rp1.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3HTMOSDSDT.rp1_[[:space:]]Three-hour[[:space:]]Timeframe[[:space:]]Market[[:space:]]Order[[:space:]]Strategy[[:space:]]with[[:space:]]Defined[[:space:]]Stoploss[[:space:]]and[[:space:]]Dynamic[[:space:]]Takeprofit/3HTMOSDSDT_rp1.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3HTMOSDSDT.rp1_[[:space:]]Three-hour[[:space:]]Timeframe[[:space:]]Market[[:space:]]Order[[:space:]]Strategy[[:space:]]with[[:space:]]Defined[[:space:]]Stoploss[[:space:]]and[[:space:]]Dynamic[[:space:]]Takeprofit/newplot[[:space:]](21).png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3HTMOSDSDT.rp1_[[:space:]]Three-hour[[:space:]]Timeframe[[:space:]]Market[[:space:]]Order[[:space:]]Strategy[[:space:]]with[[:space:]]Defined[[:space:]]Stoploss[[:space:]]and[[:space:]]Dynamic[[:space:]]Takeprofit/newplot[[:space:]](22).png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3HTMOSDSDT.rp1_[[:space:]]Three-hour[[:space:]]Timeframe[[:space:]]Market[[:space:]]Order[[:space:]]Strategy[[:space:]]with[[:space:]]Defined[[:space:]]Stoploss[[:space:]]and[[:space:]]Dynamic[[:space:]]Takeprofit/newplot[[:space:]](23).png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3HTMOSDSDT.rp1_[[:space:]]Three-hour[[:space:]]Timeframe[[:space:]]Market[[:space:]]Order[[:space:]]Strategy[[:space:]]with[[:space:]]Defined[[:space:]]Stoploss[[:space:]]and[[:space:]]Dynamic[[:space:]]Takeprofit/newplot[[:space:]](24).png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3HTMOSDSDT.rp2[[:space:]]_[[:space:]]Portfolio[[:space:]]Diversification[[:space:]]for[[:space:]]’Three-hour[[:space:]]Timeframe[[:space:]]Market[[:space:]]Order[[:space:]]Strategy[[:space:]]with[[:space:]]Defined[[:space:]]Stoploss[[:space:]]and[[:space:]]Dynamic[[:space:]]Takeprofit’[[:space:]]Trading[[:space:]]Model/3HTMOSDSDT_rp2.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/3mTSHSUHC.rp1.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]3mTSHSUHCv1.ipynb filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp2_[[:space:]]Stress[[:space:]]Test[[:space:]]&[[:space:]]Evaluating[[:space:]]the[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/3mTSHSUHC.rp2.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp2_[[:space:]]Stress[[:space:]]Test[[:space:]]&[[:space:]]Evaluating[[:space:]]the[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/Heatmap[[:space:]]Candles[[:space:]]Chart[[:space:]]with[[:space:]]Trade[[:space:]]Executions.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp3[[:space:]]_[[:space:]]Metric[[:space:]]Threshold[[:space:]]and[[:space:]]Minimum[[:space:]]Bars[[:space:]]Apart[[:space:]]Analysis[[:space:]]of[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/3mTSHSUHC.rp3.pdf filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp3[[:space:]]_[[:space:]]Metric[[:space:]]Threshold[[:space:]]and[[:space:]]Minimum[[:space:]]Bars[[:space:]]Apart[[:space:]]Analysis[[:space:]]of[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/the[[:space:]]five[[:space:]]3D[[:space:]]graphs.ipynb filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp3[[:space:]]_[[:space:]]Metric[[:space:]]Threshold[[:space:]]and[[:space:]]Minimum[[:space:]]Bars[[:space:]]Apart[[:space:]]Analysis[[:space:]]of[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/the[[:space:]]3d[[:space:]]graphs[[:space:]]plotting.ipynb filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]3mTSHSUHC.rp3[[:space:]]_[[:space:]]Metric[[:space:]]Threshold[[:space:]]and[[:space:]]Minimum[[:space:]]Bars[[:space:]]Apart[[:space:]]Analysis[[:space:]]of[[:space:]]Three-minute[[:space:]]Timeframe[[:space:]]Stoporder[[:space:]]Hedging[[:space:]]Strategy[[:space:]]Using[[:space:]]Heatmap[[:space:]]Candles/Draft/draft1.ipynb filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/1HRMRSS_MCS1000_profitableEquityCurves.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/1HRMRSS_MCS1000_unprofitableEquityCurves.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_00-01.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_01-02.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_02-03.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_03-04.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_04-05.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_05-06.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_06-07.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_09-10.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_10-11.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_11-12.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_12-13.png filter=lfs diff=lfs merge=lfs -text
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| 50 |
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_13-14.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_14-15.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_15-16.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_16-17.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_17-18.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_18-19.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_19-20.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_20-21.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/avg_total.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1HRMRSS.rp1_[[:space:]]Design[[:space:]]and[[:space:]]Optimization[[:space:]]of[[:space:]]the[[:space:]]One[[:space:]]Hour[[:space:]]Range[[:space:]]Median[[:space:]]Reversion[[:space:]]Stoporder[[:space:]]Strategy/Performance[[:space:]]img/overview.png filter=lfs diff=lfs merge=lfs -text
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Research[[:space:]]Papers/The[[:space:]]1CPMOTS.rp1_[[:space:]]Is[[:space:]]Treating[[:space:]]Trading[[:space:]]Worse[[:space:]]Than[[:space:]]a[[:space:]]Coin[[:space:]]Flip,[[:space:]]but[[:space:]]with[[:space:]]a[[:space:]]Slight[[:space:]]Edge,[[:space:]]Profitable_/codes/1candlepattern.ipynb filter=lfs diff=lfs merge=lfs -text
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Python[[:space:]]Folder/All[[:space:]]country's[[:space:]]Total[[:space:]]Debt/Worldwind[[:space:]]Yearly[[:space:]]Debt[[:space:]](1).ipynb filter=lfs diff=lfs merge=lfs -text
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Python[[:space:]]Folder/Volatility[[:space:]]Based[[:space:]]Trading[[:space:]]Strategy,[[:space:]]VTR,[[:space:]]ADX,[[:space:]]DI,[[:space:]]DMag/Volatility[[:space:]]Based[[:space:]]Trading[[:space:]]Strategy,[[:space:]]VTR,[[:space:]]ADX,[[:space:]]DI,[[:space:]]DMag.pdf filter=lfs diff=lfs merge=lfs -text
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Python[[:space:]]Folder/Volatility[[:space:]]Based[[:space:]]Trading[[:space:]]Strategy,[[:space:]]VTR,[[:space:]]ADX,[[:space:]]DI,[[:space:]]DMag/xau_system_signals.png filter=lfs diff=lfs merge=lfs -text
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Pinescript Folder/Verttical Lines at Specific Time/Vertical lines at specific time.ipynb
ADDED
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| 1 |
+
{
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| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": null,
|
| 6 |
+
"id": "0a297891",
|
| 7 |
+
"metadata": {
|
| 8 |
+
"vscode": {
|
| 9 |
+
"languageId": "plaintext"
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| 10 |
+
}
|
| 11 |
+
},
|
| 12 |
+
"outputs": [],
|
| 13 |
+
"source": [
|
| 14 |
+
"//@version=5\n",
|
| 15 |
+
"indicator(\"vertical lines\", overlay=true)\n",
|
| 16 |
+
"\n",
|
| 17 |
+
"// Only show vertical lines if timeframe is intraday (less than daily)\n",
|
| 18 |
+
"showLines = timeframe.isintraday\n",
|
| 19 |
+
"\n",
|
| 20 |
+
"// Convert current bar time to Asia/Manila timezone\n",
|
| 21 |
+
"t = time(timeframe.period, \"Asia/Manila\")\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"// Target hours excluding 0, 12, 20\n",
|
| 24 |
+
"var int[] targetHours = array.from(3, 6, 8, 15, 24) // 24 means midnight (0h)\n",
|
| 25 |
+
"\n",
|
| 26 |
+
"// Special hours separated\n",
|
| 27 |
+
"specialHour20 = 20\n",
|
| 28 |
+
"specialHour0 = 0 // midnight\n",
|
| 29 |
+
"specialHour12 = 12 // noon\n",
|
| 30 |
+
"\n",
|
| 31 |
+
"// Function to check if current bar time matches any target hour exactly at minute zero\n",
|
| 32 |
+
"isTargetTime() =>\n",
|
| 33 |
+
" h = hour(t)\n",
|
| 34 |
+
" m = minute(t)\n",
|
| 35 |
+
" match = false\n",
|
| 36 |
+
" for i = 0 to array.size(targetHours) - 1\n",
|
| 37 |
+
" if (h == array.get(targetHours, i) or (h == 0 and array.get(targetHours, i) == 24)) and m == 0\n",
|
| 38 |
+
" match := true\n",
|
| 39 |
+
" match\n",
|
| 40 |
+
"\n",
|
| 41 |
+
"// Check if current time matches special hour 20 at minute zero\n",
|
| 42 |
+
"isSpecialTime20() =>\n",
|
| 43 |
+
" h = hour(t)\n",
|
| 44 |
+
" m = minute(t)\n",
|
| 45 |
+
" (h == specialHour20) and (m == 0)\n",
|
| 46 |
+
"\n",
|
| 47 |
+
"// Check if current time matches special hour 0 at minute zero\n",
|
| 48 |
+
"isSpecialTime0() =>\n",
|
| 49 |
+
" h = hour(t)\n",
|
| 50 |
+
" m = minute(t)\n",
|
| 51 |
+
" (h == specialHour0) and (m == 0)\n",
|
| 52 |
+
"\n",
|
| 53 |
+
"// Check if current time matches special hour 12 at minute zero\n",
|
| 54 |
+
"isSpecialTime12() =>\n",
|
| 55 |
+
" h = hour(t)\n",
|
| 56 |
+
" m = minute(t)\n",
|
| 57 |
+
" (h == specialHour12) and (m == 0)\n",
|
| 58 |
+
"\n",
|
| 59 |
+
"// Detect new day (day break)\n",
|
| 60 |
+
"newDay = ta.change(time(\"D\"))\n",
|
| 61 |
+
"\n",
|
| 62 |
+
"// Only draw lines if timeframe is intraday\n",
|
| 63 |
+
"if showLines\n",
|
| 64 |
+
" // Draw vertical line at day break (red, width 1)\n",
|
| 65 |
+
" if newDay\n",
|
| 66 |
+
" line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.green, width=1, extend=extend.both)\n",
|
| 67 |
+
"\n",
|
| 68 |
+
" // Draw vertical line at target times (blue, width 1)\n",
|
| 69 |
+
" if isTargetTime()\n",
|
| 70 |
+
" line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.rgb(33, 149, 243, 90), width=1, extend=extend.both)\n",
|
| 71 |
+
"\n",
|
| 72 |
+
" // Draw vertical line at special hour 20 (violet, width 1)\n",
|
| 73 |
+
" if isSpecialTime20()\n",
|
| 74 |
+
" line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.new(color.teal, 0), width=1, extend=extend.both)\n",
|
| 75 |
+
"\n",
|
| 76 |
+
" // Draw vertical line at special hour 0 (midnight) - orange, width 2, dotted\n",
|
| 77 |
+
" if isSpecialTime0()\n",
|
| 78 |
+
" var line midnightLine = na\n",
|
| 79 |
+
" midnightLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.orange, width=1, extend=extend.both)\n",
|
| 80 |
+
" line.set_style(midnightLine, line.style_dotted)\n",
|
| 81 |
+
"\n",
|
| 82 |
+
" // Draw vertical line at special hour 12 (noon) - green, width 2, dotted \n",
|
| 83 |
+
" if isSpecialTime12()\n",
|
| 84 |
+
" var line noonLine = na\n",
|
| 85 |
+
" noonLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.red, width=1, extend=extend.both)\n",
|
| 86 |
+
" line.set_style(noonLine, line.style_dotted)\n"
|
| 87 |
+
]
|
| 88 |
+
},
|
| 89 |
+
{
|
| 90 |
+
"attachments": {
|
| 91 |
+
"image.png": {
|
| 92 |
+
"image/png": 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"
|
| 93 |
+
}
|
| 94 |
+
},
|
| 95 |
+
"cell_type": "markdown",
|
| 96 |
+
"id": "fff3f0fe",
|
| 97 |
+
"metadata": {},
|
| 98 |
+
"source": [
|
| 99 |
+
""
|
| 100 |
+
]
|
| 101 |
+
},
|
| 102 |
+
{
|
| 103 |
+
"cell_type": "markdown",
|
| 104 |
+
"id": "8d04214c",
|
| 105 |
+
"metadata": {},
|
| 106 |
+
"source": []
|
| 107 |
+
}
|
| 108 |
+
],
|
| 109 |
+
"metadata": {
|
| 110 |
+
"language_info": {
|
| 111 |
+
"name": "python"
|
| 112 |
+
}
|
| 113 |
+
},
|
| 114 |
+
"nbformat": 4,
|
| 115 |
+
"nbformat_minor": 5
|
| 116 |
+
}
|
Pinescript Folder/Vison Market Profile/vison market profile.ipynb
ADDED
|
@@ -0,0 +1,452 @@
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": null,
|
| 6 |
+
"id": "9d6c0e6d",
|
| 7 |
+
"metadata": {
|
| 8 |
+
"vscode": {
|
| 9 |
+
"languageId": "plaintext"
|
| 10 |
+
}
|
| 11 |
+
},
|
| 12 |
+
"outputs": [],
|
| 13 |
+
"source": [
|
| 14 |
+
"//@version=5\n",
|
| 15 |
+
"indicator(\"Volume Profile and Indicator by Sunstoic\", \"v5 Vison Market Profile\", overlay = true, max_boxes_count = 500, max_lines_count = 500, max_bars_back = 5000)\n",
|
| 16 |
+
"\n",
|
| 17 |
+
"\n",
|
| 18 |
+
"// Inputs\n",
|
| 19 |
+
"disp = display.all - display.status_line\n",
|
| 20 |
+
"\n",
|
| 21 |
+
"vpGR = 'Volume & Sentiment Profile'\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"vpTP = 'displays total trading activity (both buying and selling trading activity) over a specified time period at specific price levels\\n\\n' +\n",
|
| 24 |
+
" 'row lengths, indicates the amount of the traded activity at specific price levels\\n\\n' +\n",
|
| 25 |
+
" ' - high traded zones - usually represents consolidation zones (value areas)\\n' +\n",
|
| 26 |
+
" ' - low traded zones - usually represents supply & demand or liquidity zones'\n",
|
| 27 |
+
" \n",
|
| 28 |
+
"vpSH = input.bool(true, 'Volume Profile', group = vpGR, tooltip = vpTP)\n",
|
| 29 |
+
"\n",
|
| 30 |
+
"vpUC = input.color(color.new(#000000, 50), ' Up Volume ', inline = 'VP', group = vpGR)\n",
|
| 31 |
+
"vpDC = input.color(color.new(#000000, 50), 'Down Volume ', inline = 'VP', group = vpGR)\n",
|
| 32 |
+
"\n",
|
| 33 |
+
"vaUC = input.color(color.new(#000000, 50), ' Value Area Up', inline = 'VA', group = vpGR)\n",
|
| 34 |
+
"vaDC = input.color(color.new(#000000, 50), 'Value Area Down', inline = 'VA', group = vpGR)\n",
|
| 35 |
+
"\n",
|
| 36 |
+
"\n",
|
| 37 |
+
"// User Inputs\n",
|
| 38 |
+
"pcSH = input.string('Developing POC', 'Point of Control', options = ['Developing POC', 'Last (Line)', 'None'], inline = 'POC', group = vpGR, display = disp)\n",
|
| 39 |
+
"pocC = input.color(color.rgb(0, 0, 0), '', inline = 'POC', group = vpGR)\n",
|
| 40 |
+
"pocW = input.int(2, '', minval = 1, inline = 'POC', group = vpGR, display = disp)\n",
|
| 41 |
+
"\n",
|
| 42 |
+
"// Mock Developing POC (replace with your own logic)\n",
|
| 43 |
+
"var float developingPOC = na\n",
|
| 44 |
+
"developingPOC := (high + low / 2) // For demo purposes, this just uses the close\n",
|
| 45 |
+
"\n",
|
| 46 |
+
"// Plot the POC using stepline if enabled\n",
|
| 47 |
+
"plot(pcSH == 'Developing POC' ? developingPOC : na,title = 'Developing POC', color = pocC,linewidth = pocW,style = plot.style_stepline)\n",
|
| 48 |
+
"\n",
|
| 49 |
+
"vpVA = input.float(68, 'Value Area (%)', minval = 0, maxval = 100, group = vpGR, display = disp) / 100\n",
|
| 50 |
+
"\n",
|
| 51 |
+
"vahS = input.bool(true, 'Value Area High (VAH)', inline = 'VAH', group = vpGR)\n",
|
| 52 |
+
"vahC = input.color(#2962ff, '', inline = 'VAH', group = vpGR)\n",
|
| 53 |
+
"vahW = input.int(1, '', minval = 1, inline = 'VAH', group = vpGR, display = disp)\n",
|
| 54 |
+
"\n",
|
| 55 |
+
"vlSH = input.bool(true, 'Value Area Low (VAL)', inline = 'VAL', group = vpGR)\n",
|
| 56 |
+
"valC = input.color(#2962ff, '', inline = 'VAL', group = vpGR)\n",
|
| 57 |
+
"valW = input.int(1, '', minval = 1, inline = 'VAL', group = vpGR, display = disp)\n",
|
| 58 |
+
"\n",
|
| 59 |
+
"spPT = 'polarity methods is a measure used to divide the total volume into either up volume (trades that moved the price up) or ' + \n",
|
| 60 |
+
" 'down volume (trades that moved the price down), simply said conditions used to calculate up/down volume\\n\\n' +\n",
|
| 61 |
+
" '* bar polarity\\n up => if close > open\\n down => if close <= open\\n\\n' +\n",
|
| 62 |
+
" '* bar buying/selling pressure\\n up => if (close - low) > (high - close)\\n down => if (close - low) <= (high - close)'\n",
|
| 63 |
+
"spP1 = 'Bar Polarity'\n",
|
| 64 |
+
"spP2 = 'Bar Buying/Selling Pressure'\n",
|
| 65 |
+
"vpPT = input.string(spP1, 'Profile Polarity Method', options = [spP1, spP2], group = vpGR, tooltip = spPT, display = disp)\n",
|
| 66 |
+
"vsPT = vpPT == spP1\n",
|
| 67 |
+
"\n",
|
| 68 |
+
"vpLR = input.string('Visible Range', 'Profile Lookback Range', options = ['Fixed Range', 'Visible Range'], group = vpGR, display = disp)\n",
|
| 69 |
+
"vpRL = vpLR == 'Visible Range'\n",
|
| 70 |
+
"vpLT = 'applicable when \\'Lookback Range\\' is selected as \\'Fixed Range\\''\n",
|
| 71 |
+
"vpLN = input.int(360, 'Lookback Length / Fixed Range', minval = 10, maxval = 5000, step = 10, group = vpGR, tooltip = vpLT, display = disp)\n",
|
| 72 |
+
"vpLN:= last_bar_index < vpLN ? last_bar_index : vpLN - 1\n",
|
| 73 |
+
"\n",
|
| 74 |
+
"vpST = input.bool(true, 'Profile Stats', inline = 'STT', group = vpGR, display = disp)\n",
|
| 75 |
+
"ppLS = input.string('Small', \"\", options = ['Tiny', 'Small', 'Normal'], inline = 'STT', group = vpGR, display = disp)\n",
|
| 76 |
+
"lcDB = input.string('Top Right', '', options = ['Top Right', 'Middle Right', 'Bottom Left'], inline = 'STT', group = vpGR, display = disp)\n",
|
| 77 |
+
"\n",
|
| 78 |
+
"vpLV = input.bool(true, 'Profile Price Levels', inline = 'BBe', group = vpGR)\n",
|
| 79 |
+
"rpLS = input.string('Small', \"\", options=['Tiny', 'Small', 'Normal'], inline = 'BBe', group = vpGR, display = disp)\n",
|
| 80 |
+
"\n",
|
| 81 |
+
"vpPL = input.string('Right', 'Profile Placement', options = ['Right', 'Left'], group = vpGR, display = disp)\n",
|
| 82 |
+
"vpRT = vpPL == 'Right' \n",
|
| 83 |
+
"vpNR = input.int(250, 'Profile Number of Rows' , minval = 10, maxval = 500 , step = 10, group = vpGR, display = disp)\n",
|
| 84 |
+
"vpWD = input.float(31, 'Profile Width', minval = 0, maxval = 250, group = vpGR, display = disp) / 100\n",
|
| 85 |
+
"vpHO = input.int(0, 'Profile Horizontal Offset', maxval = 50, group = vpGR, display = disp)\n",
|
| 86 |
+
"\n",
|
| 87 |
+
"vaBG = input.bool(false, 'Value Area Background ', inline = 'vBG', group = vpGR)\n",
|
| 88 |
+
"vBGC = input.color(color.new(#2962ff, 89), '', inline = 'vBG', group = vpGR)\n",
|
| 89 |
+
"\n",
|
| 90 |
+
"vpBG = input.bool(false, 'Profile Range Background ', inline = 'pBG', group = vpGR)\n",
|
| 91 |
+
"bgC = input.color(color.new(#2962ff, 95), '', inline = 'pBG', group = vpGR)\n",
|
| 92 |
+
"\n",
|
| 93 |
+
"\n",
|
| 94 |
+
"cbGR = 'Volume Weighted Colored Bars'\n",
|
| 95 |
+
"\n",
|
| 96 |
+
"cbTT = 'Colors bars based on the bar\\'s volume relative to volume moving average\\n' +\n",
|
| 97 |
+
" ' - Bold bars when bar\\'s volume is above volume moving average * upper threshold\\n' +\n",
|
| 98 |
+
" ' - Light bars when bar\\'s volume is below volume moving average * lower threshold'\n",
|
| 99 |
+
"\n",
|
| 100 |
+
"vwcb = input.bool(false, 'Volume Weighted Colored Bars', group = cbGR, tooltip = cbTT)\n",
|
| 101 |
+
"upTH = input.float(1.618, ' Upper Threshold', minval=1., step=.1, group = cbGR, display = disp)\n",
|
| 102 |
+
"dnTH = input.float(0.618, ' Lower Threshold', minval=.1, step=.1, group = cbGR, display = disp) \n",
|
| 103 |
+
"\n",
|
| 104 |
+
"\n",
|
| 105 |
+
"// User Defined Types\n",
|
| 106 |
+
"type bar\n",
|
| 107 |
+
" float o = open\n",
|
| 108 |
+
" float h = high\n",
|
| 109 |
+
" float l = low\n",
|
| 110 |
+
" float c = close\n",
|
| 111 |
+
" float v = volume\n",
|
| 112 |
+
" int i = bar_index\n",
|
| 113 |
+
"\n",
|
| 114 |
+
"type barData\n",
|
| 115 |
+
" float [] bh\n",
|
| 116 |
+
" float [] bl\n",
|
| 117 |
+
" float [] bv\n",
|
| 118 |
+
" bool [] bp\n",
|
| 119 |
+
" int [] bn\n",
|
| 120 |
+
"\n",
|
| 121 |
+
"type volData\n",
|
| 122 |
+
" float [] vt\n",
|
| 123 |
+
" float [] vb\n",
|
| 124 |
+
" float [] vd\n",
|
| 125 |
+
"\n",
|
| 126 |
+
"type tVP\n",
|
| 127 |
+
" box [] vp\n",
|
| 128 |
+
" chart.point [] pPC\n",
|
| 129 |
+
" polyline dPC\n",
|
| 130 |
+
" int pcL\n",
|
| 131 |
+
" int laP\n",
|
| 132 |
+
" int lbP\n",
|
| 133 |
+
" int sI\n",
|
| 134 |
+
"\n",
|
| 135 |
+
"type tVH\n",
|
| 136 |
+
" line [] vh\n",
|
| 137 |
+
" chart.point [] pMA\n",
|
| 138 |
+
" polyline vMA\n",
|
| 139 |
+
"\n",
|
| 140 |
+
"\n",
|
| 141 |
+
"// Variables\n",
|
| 142 |
+
"var barData bD = barData.new(\n",
|
| 143 |
+
" array.new <float> (na), \n",
|
| 144 |
+
" array.new <float> (na), \n",
|
| 145 |
+
" array.new <float> (na), \n",
|
| 146 |
+
" array.new <bool> (na), \n",
|
| 147 |
+
" array.new <int> (na)\n",
|
| 148 |
+
" )\n",
|
| 149 |
+
"\n",
|
| 150 |
+
"volData vD = volData.new(\n",
|
| 151 |
+
" array.new <float> (vpNR, 0.), \n",
|
| 152 |
+
" array.new <float> (vpNR, 0.), \n",
|
| 153 |
+
" array.new <float> (vpNR, 0.)\n",
|
| 154 |
+
" )\n",
|
| 155 |
+
"\n",
|
| 156 |
+
"var tVP VP = tVP.new(\n",
|
| 157 |
+
" array.new<box> (na),\n",
|
| 158 |
+
" array.new<chart.point> (na),\n",
|
| 159 |
+
" polyline.new (na), na, na, na, na\n",
|
| 160 |
+
" )\n",
|
| 161 |
+
"\n",
|
| 162 |
+
"var tVH VH = tVH.new(\n",
|
| 163 |
+
" array.new<line> (na),\n",
|
| 164 |
+
" array.new<chart.point> (na),\n",
|
| 165 |
+
" polyline.new (na)\n",
|
| 166 |
+
" )\n",
|
| 167 |
+
"\n",
|
| 168 |
+
"bar b = bar.new()\n",
|
| 169 |
+
"bar [] ltfBD = array.new<bar> (1, bar.new())\n",
|
| 170 |
+
"\n",
|
| 171 |
+
"var float pHST = na\n",
|
| 172 |
+
"var float pLST = na\n",
|
| 173 |
+
"var string ltf = na\n",
|
| 174 |
+
"\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"// Functions / Methods\n",
|
| 177 |
+
"f_drawLineX(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width) =>\n",
|
| 178 |
+
" var id = line.new(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width)\n",
|
| 179 |
+
" line.set_xy1(id, _x1, _y1)\n",
|
| 180 |
+
" line.set_xy2(id, _x2, _y2)\n",
|
| 181 |
+
" line.set_color(id, _color)\n",
|
| 182 |
+
"\n",
|
| 183 |
+
"f_drawLabelX(_x, _y, _text, _color, _style, _textcolor, _size, _tooltip) =>\n",
|
| 184 |
+
" var lb = label.new(_x, _y, _text, xloc.bar_index, yloc.price, _color, _style, _textcolor, _size, text.align_left, _tooltip)\n",
|
| 185 |
+
" lb.set_xy(_x, _y)\n",
|
| 186 |
+
" lb.set_text(_text)\n",
|
| 187 |
+
" lb.set_tooltip(_tooltip)\n",
|
| 188 |
+
" lb.set_textcolor(_textcolor)\n",
|
| 189 |
+
"\n",
|
| 190 |
+
"getData(_ltf) => request.security_lower_tf(syminfo.tickerid, _ltf, bar.new(), ignore_invalid_timeframe = true)\n",
|
| 191 |
+
"\n",
|
| 192 |
+
"f_gTF(_d) => \n",
|
| 193 |
+
" int tfInMs = timeframe.in_seconds(timeframe.period)\n",
|
| 194 |
+
" int mInMS = 60\n",
|
| 195 |
+
"\n",
|
| 196 |
+
" if _d == 2\n",
|
| 197 |
+
" switch\n",
|
| 198 |
+
" tfInMs < 30 => '1S'\n",
|
| 199 |
+
" tfInMs < 1 * mInMS => '5S'\n",
|
| 200 |
+
"\n",
|
| 201 |
+
" tfInMs <= 15 * mInMS => '1'\n",
|
| 202 |
+
" tfInMs <= 60 * mInMS => '5'\n",
|
| 203 |
+
" tfInMs <= 240 * mInMS => '15'\n",
|
| 204 |
+
" tfInMs <= 1440 * mInMS => '60'\n",
|
| 205 |
+
" => 'D'\n",
|
| 206 |
+
"\n",
|
| 207 |
+
" else if _d == 1\n",
|
| 208 |
+
" switch\n",
|
| 209 |
+
" tfInMs < 15 => '1S'\n",
|
| 210 |
+
" tfInMs < 30 => '5S'\n",
|
| 211 |
+
" tfInMs < 1 * mInMS => '15S'\n",
|
| 212 |
+
"\n",
|
| 213 |
+
" tfInMs <= 5 * mInMS => '1'\n",
|
| 214 |
+
" tfInMs <= 15 * mInMS => '5'\n",
|
| 215 |
+
" tfInMs <= 60 * mInMS => '15'\n",
|
| 216 |
+
" tfInMs <= 240 * mInMS => '60'\n",
|
| 217 |
+
" tfInMs <= 1440 * mInMS => '240'\n",
|
| 218 |
+
" => 'D'\n",
|
| 219 |
+
"\n",
|
| 220 |
+
"f_gTS(_t) =>\n",
|
| 221 |
+
" switch _t\n",
|
| 222 |
+
" 'Tiny' => size.tiny\n",
|
| 223 |
+
" 'Small' => size.small \n",
|
| 224 |
+
" 'Normal' => size.normal\n",
|
| 225 |
+
" => size.auto\n",
|
| 226 |
+
"\n",
|
| 227 |
+
"f_crossingLevelX(_price, _level) =>\n",
|
| 228 |
+
" (_level > _price and _level < _price[1]) or (_level < _price and _level > _price[1])\n",
|
| 229 |
+
"\n",
|
| 230 |
+
"alarm(_message) => \n",
|
| 231 |
+
" alert(syminfo.ticker + ' ' + _message + ', price ' + str.tostring(b.c, format.mintick) + ', timeframe ' + timeframe.period, alert.freq_once_per_bar)\n",
|
| 232 |
+
"\n",
|
| 233 |
+
"\n",
|
| 234 |
+
"// Calculations Volume Profile\n",
|
| 235 |
+
"nzV = nz(b.v)\n",
|
| 236 |
+
"\n",
|
| 237 |
+
"rpS = f_gTS(rpLS)\n",
|
| 238 |
+
"ppLS:= f_gTS(ppLS)\n",
|
| 239 |
+
"\n",
|
| 240 |
+
"tPOS = lcDB == 'Top Right' ? position.top_right\n",
|
| 241 |
+
" : lcDB == 'Middle Right' ? position.middle_right \n",
|
| 242 |
+
" : position.bottom_left\n",
|
| 243 |
+
"\n",
|
| 244 |
+
"if time == chart.left_visible_bar_time\n",
|
| 245 |
+
" VP.sI := b.i\n",
|
| 246 |
+
"\n",
|
| 247 |
+
"if vpRL\n",
|
| 248 |
+
" vpLN := last_bar_index - VP.sI\n",
|
| 249 |
+
"\n",
|
| 250 |
+
"if b.i == last_bar_index - vpLN\n",
|
| 251 |
+
" VP.sI := b.i\n",
|
| 252 |
+
" pLST := b.l \n",
|
| 253 |
+
" pHST := b.h\n",
|
| 254 |
+
"else if b.i > last_bar_index - vpLN\n",
|
| 255 |
+
" pLST := math.min(b.l, pLST)\n",
|
| 256 |
+
" pHST := math.max(b.h, pHST)\n",
|
| 257 |
+
"\n",
|
| 258 |
+
"if vpLN <= 200\n",
|
| 259 |
+
" ltf := f_gTF(2)\n",
|
| 260 |
+
" ltfBD := getData(f_gTF(2)) \n",
|
| 261 |
+
"else if vpLN <= 700\n",
|
| 262 |
+
" ltf := f_gTF(1)\n",
|
| 263 |
+
" ltfBD := getData(f_gTF(1)) \n",
|
| 264 |
+
"else\n",
|
| 265 |
+
" ltf := 'Chart'\n",
|
| 266 |
+
" ltfBD := array.new<bar> (1, bar.new(b.o, b.h, b.l, b.c, b.v))\n",
|
| 267 |
+
"\n",
|
| 268 |
+
"if barstate.ishistory and (b.i >= last_bar_index - vpLN) and b.i < last_bar_index and ltfBD.size() > 0\n",
|
| 269 |
+
"\n",
|
| 270 |
+
" if ltfBD.size() > 0 and not na(nz(ltfBD.get(0).v))\n",
|
| 271 |
+
" for i = 0 to ltfBD.size() - 1\n",
|
| 272 |
+
" bD.bh.push(ltfBD.get(i).h)\n",
|
| 273 |
+
" bD.bl.push(ltfBD.get(i).l)\n",
|
| 274 |
+
" bD.bv.push(ltfBD.get(i).v)\n",
|
| 275 |
+
"\n",
|
| 276 |
+
" if vsPT\n",
|
| 277 |
+
" bD.bp.push(ltfBD.get(i).c > ltfBD.get(i).o)\n",
|
| 278 |
+
" else\n",
|
| 279 |
+
" bD.bp.push(ltfBD.get(i).c - ltfBD.get(i).l > ltfBD.get(i).h - ltfBD.get(i).c)\n",
|
| 280 |
+
"\n",
|
| 281 |
+
" bD.bn.push(ltfBD.size())\n",
|
| 282 |
+
"\n",
|
| 283 |
+
"pSTP = (pHST - pLST) / vpNR\n",
|
| 284 |
+
"\n",
|
| 285 |
+
"if barstate.islast and ltfBD.size() > 0\n",
|
| 286 |
+
"\n",
|
| 287 |
+
" if VP.vp.size() > 0\n",
|
| 288 |
+
" for i = 0 to VP.vp.size() - 1\n",
|
| 289 |
+
" box.delete(VP.vp.shift())\n",
|
| 290 |
+
"\n",
|
| 291 |
+
" if bD.bn.size() > vpLN\n",
|
| 292 |
+
" qt = bD.bn.shift()\n",
|
| 293 |
+
" for i = 0 to qt - 1\n",
|
| 294 |
+
" bD.bh.shift()\n",
|
| 295 |
+
" bD.bl.shift()\n",
|
| 296 |
+
" bD.bv.shift()\n",
|
| 297 |
+
" bD.bp.shift()\n",
|
| 298 |
+
"\n",
|
| 299 |
+
" VP.pPC.clear()\n",
|
| 300 |
+
" VP.dPC.delete()\n",
|
| 301 |
+
"\n",
|
| 302 |
+
" if ltfBD.size() > 0 and not na(nz(ltfBD.get(0).v))\n",
|
| 303 |
+
" for i = 0 to ltfBD.size() - 1\n",
|
| 304 |
+
" bD.bh.push(ltfBD.get(i).h)\n",
|
| 305 |
+
" bD.bl.push(ltfBD.get(i).l)\n",
|
| 306 |
+
" bD.bv.push(ltfBD.get(i).v)\n",
|
| 307 |
+
"\n",
|
| 308 |
+
" if vsPT\n",
|
| 309 |
+
" bD.bp.push(ltfBD.get(i).c > ltfBD.get(i).o)\n",
|
| 310 |
+
" else\n",
|
| 311 |
+
" bD.bp.push(ltfBD.get(i).c - ltfBD.get(i).l > ltfBD.get(i).h - ltfBD.get(i).c)\n",
|
| 312 |
+
"\n",
|
| 313 |
+
" bD.bn.push(ltfBD.size())\n",
|
| 314 |
+
"\n",
|
| 315 |
+
" bI = vpLN\n",
|
| 316 |
+
" bSZ = 0\n",
|
| 317 |
+
" aSZ = bD.bv.size()\n",
|
| 318 |
+
"\n",
|
| 319 |
+
" for aI = 0 to aSZ - 1\n",
|
| 320 |
+
"\n",
|
| 321 |
+
" i = 0\n",
|
| 322 |
+
" for pLL = pLST to pHST - pSTP by pSTP\n",
|
| 323 |
+
"\n",
|
| 324 |
+
" lH = bD.bh.get(aI)\n",
|
| 325 |
+
" lL = bD.bl.get(aI)\n",
|
| 326 |
+
" lV = bD.bv.get(aI)\n",
|
| 327 |
+
"\n",
|
| 328 |
+
" if lH >= pLL and lL < pLL + pSTP\n",
|
| 329 |
+
"\n",
|
| 330 |
+
" vPOR = if lL >= pLL and lH > pLL + pSTP\n",
|
| 331 |
+
" (pLL + pSTP - lL) / (lH - lL)\n",
|
| 332 |
+
" else if lH <= pLL + pSTP and lL < pLL\n",
|
| 333 |
+
" (lH - pLL) / (lH - lL)\n",
|
| 334 |
+
" else if lL >= pLL and lH <= pLL + pSTP\n",
|
| 335 |
+
" 1\n",
|
| 336 |
+
" else\n",
|
| 337 |
+
" pSTP / (lH - lL)\n",
|
| 338 |
+
"\n",
|
| 339 |
+
" vD.vt.set(i, vD.vt.get(i) + lV * vPOR)\n",
|
| 340 |
+
"\n",
|
| 341 |
+
" if bD.bp.get(aI)\n",
|
| 342 |
+
" vD.vb.set(i, vD.vb.get(i) + lV * vPOR)\n",
|
| 343 |
+
" i += 1\n",
|
| 344 |
+
"\n",
|
| 345 |
+
" if pcSH == 'Developing POC'\n",
|
| 346 |
+
" if aI == bD.bn.get(vpLN - bI)\n",
|
| 347 |
+
" VP.pPC.push(chart.point.from_index(b.i[bI], math.avg(b.h[bI], b.l[bI])))\n",
|
| 348 |
+
" VP.pPC.push(chart.point.from_index(b.i[bI] + 1, pLST + (vD.vt.indexof(vD.vt.max()) + .5) * pSTP))\n",
|
| 349 |
+
" bSZ += bD.bn.get(vpLN - bI)\n",
|
| 350 |
+
" bI -= 1\n",
|
| 351 |
+
" else if aI == (bSZ + bD.bn.get(vpLN - bI)) and bSZ != 0\n",
|
| 352 |
+
" VP.pPC.push(chart.point.from_index(b.i[bI] + 1, pLST + (vD.vt.indexof(vD.vt.max()) + .5) * pSTP))\n",
|
| 353 |
+
" bSZ += bD.bn.get(vpLN - bI)\n",
|
| 354 |
+
" bI -= 1\n",
|
| 355 |
+
" else if bI == 0\n",
|
| 356 |
+
" VP.pPC.push(chart.point.from_index(b.i[bI] + 1, pLST + (vD.vt.indexof(vD.vt.max()) + .5) * pSTP))\n",
|
| 357 |
+
" bSZ += bD.bn.get(vpLN - bI)\n",
|
| 358 |
+
"\n",
|
| 359 |
+
" VP.dPC := polyline.new(VP.pPC, false, false, xloc.bar_index, pocC, color(na), line.style_solid, pocW)\n",
|
| 360 |
+
"\n",
|
| 361 |
+
" for i = 0 to vpNR - 1\n",
|
| 362 |
+
" bbp = 2 * vD.vb.get(i) - vD.vt.get(i)\n",
|
| 363 |
+
" vD.vd.set(i, vD.vd.get(i) + bbp * (bbp > 0 ? 1 : -1) )\n",
|
| 364 |
+
"\n",
|
| 365 |
+
" VP.pcL := vD.vt.indexof(vD.vt.max())\n",
|
| 366 |
+
" ttV = vD.vt.sum() * vpVA\n",
|
| 367 |
+
" va = vD.vt.get(VP.pcL)\n",
|
| 368 |
+
" VP.laP := VP.pcL\n",
|
| 369 |
+
" VP.lbP := VP.pcL\n",
|
| 370 |
+
" \n",
|
| 371 |
+
" while va < ttV\n",
|
| 372 |
+
" if VP.lbP == 0 and VP.laP == vpNR - 1\n",
|
| 373 |
+
" break\n",
|
| 374 |
+
"\n",
|
| 375 |
+
" vaP = 0.\n",
|
| 376 |
+
" if VP.laP < vpNR - 1 \n",
|
| 377 |
+
" vaP := vD.vt.get(VP.laP + 1)\n",
|
| 378 |
+
"\n",
|
| 379 |
+
" vbP = 0.\n",
|
| 380 |
+
" if VP.lbP > 0\n",
|
| 381 |
+
" vbP := vD.vt.get(VP.lbP - 1)\n",
|
| 382 |
+
" \n",
|
| 383 |
+
" if vaP >= vbP\n",
|
| 384 |
+
" va += vaP\n",
|
| 385 |
+
" VP.laP += 1\n",
|
| 386 |
+
" else\n",
|
| 387 |
+
" va += vbP\n",
|
| 388 |
+
" VP.lbP -= 1\n",
|
| 389 |
+
"\n",
|
| 390 |
+
" vaH = pLST + (VP.laP + 1.) * pSTP\n",
|
| 391 |
+
" poc = pLST + (VP.pcL + .5) * pSTP\n",
|
| 392 |
+
" vaL = pLST + (VP.lbP + .0) * pSTP\n",
|
| 393 |
+
" pLN = vpLN > 360 ? 360 : vpLN\n",
|
| 394 |
+
"\n",
|
| 395 |
+
" \n",
|
| 396 |
+
" if pcSH == 'Last (Line)'\n",
|
| 397 |
+
" f_drawLineX(VP.sI, poc, last_bar_index, poc, xloc.bar_index, extend.none, pocC, line.style_solid, pocW)\n",
|
| 398 |
+
"\n",
|
| 399 |
+
" \n",
|
| 400 |
+
" if vaBG\n",
|
| 401 |
+
" VP.vp.push(box.new(VP.sI, vaL, last_bar_index, vaH, vBGC, 1, line.style_dotted, bgcolor = vBGC))\n",
|
| 402 |
+
"\n",
|
| 403 |
+
" \n",
|
| 404 |
+
" for i = 0 to vpNR - 1\n",
|
| 405 |
+
" \n",
|
| 406 |
+
" if vpSH\n",
|
| 407 |
+
" sBI = vpRT ? int((pLN * vpWD + vpHO)) + int(last_bar_index - vD.vb.get(i) / vD.vt.max() * pLN * vpWD) : VP.sI\n",
|
| 408 |
+
" eBI = vpRT ? int((pLN * vpWD + vpHO)) + last_bar_index : int(sBI + vD.vb.get(i) / vD.vt.max() * pLN * vpWD)\n",
|
| 409 |
+
" VP.vp.push(box.new(sBI, pLST + (i + .1) * pSTP, eBI, pLST + (i + .9) * pSTP, color(na), bgcolor = i >= VP.lbP and i <= VP.laP ? vaUC : vpUC))\n",
|
| 410 |
+
"\n",
|
| 411 |
+
" sBI := vpRT ? sBI : eBI\n",
|
| 412 |
+
" eBI := vpRT ? sBI - int( (vD.vt.get(i) - vD.vb.get(i)) / vD.vt.max() * pLN * vpWD) : sBI + int( (vD.vt.get(i) - vD.vb.get(i)) / vD.vt.max() * pLN * vpWD)\n",
|
| 413 |
+
" VP.vp.push(box.new(sBI, pLST + (i + .1) * pSTP, eBI, pLST + (i + .9) * pSTP, color(na), bgcolor = i >= VP.lbP and i <= VP.laP ? vaDC : vpDC))\n",
|
| 414 |
+
"\n",
|
| 415 |
+
"\n",
|
| 416 |
+
"// Calculations Volume Histogram\n",
|
| 417 |
+
"pHSTv = ta.highest(b.h, vpLN < 497 ? vpLN : 496)\n",
|
| 418 |
+
"pLSTv = ta.lowest (b.l, vpLN < 497 ? vpLN : 496)\n",
|
| 419 |
+
"vHST = ta.highest(nzV, vpLN < 497 ? vpLN : 496)\n",
|
| 420 |
+
"\n",
|
| 421 |
+
"\n",
|
| 422 |
+
"\n"
|
| 423 |
+
]
|
| 424 |
+
},
|
| 425 |
+
{
|
| 426 |
+
"attachments": {
|
| 427 |
+
"image.png": {
|
| 428 |
+
"image/png": 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|
| 429 |
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}
|
| 430 |
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},
|
| 431 |
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"cell_type": "markdown",
|
| 432 |
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"id": "c4630aae",
|
| 433 |
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"metadata": {},
|
| 434 |
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"source": [
|
| 435 |
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""
|
| 436 |
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]
|
| 437 |
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},
|
| 438 |
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{
|
| 439 |
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"cell_type": "markdown",
|
| 440 |
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"id": "7209f45a",
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| 441 |
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| 442 |
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"source": []
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| 443 |
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}
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| 444 |
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],
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| 445 |
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"metadata": {
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| 446 |
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"language_info": {
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| 447 |
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"name": "python"
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| 448 |
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},
|
| 450 |
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|
| 452 |
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}
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Pinescript Folder/v5 Variant 2/fuhfuhlogic.ipynb
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|
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Pinescript Folder/v6 Sunstoic/part1.ipynb
ADDED
|
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|
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|
Python Folder/All country's Total Debt/Worldwind Yearly Debt (1).ipynb
ADDED
|
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size 10701089
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Python Folder/Volatility Based Trading Strategy, VTR, ADX, DI, DMag/Volatility Based Trading Strategy, VTR, ADX, DI, DMag.pdf
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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