Principles

Morning Routine

Tue, 10 Jun 2025 00:00:00 +0000

🌅 How I Automated My Entire Morning Routine (And You Can Too!)

I used to start every morning checking multiple apps: weather, calendar, todo lists, trying to remember what habits I wanted to track. It was inefficient and inconsistent.

So I built a personal automation system using N8N that delivers everything I need in one message at 7 AM sharp. Now my morning routine takes 30 seconds instead of 15 minutes, and I actually stick to my habits.

Smarter Loan Project Overview

Sun, 08 Jun 2025 00:00:00 +0000

Original Post

Recently, I spent a lot of time on “vibe coding”—using AI agents to create software. I’d like to share a project I worked on: www.smarter-loan.com.

Project assumptions:

Minimal self-coding: Less than 5% of the code was written by me (even the content is mostly AI-generated!).
Diverse tools 🛠️: I learned that different models excel at different tasks:
- Replit: UI
- Roo Code with Claude: architectural and mathematical aspects
- Gemini: “grunt work”
Reasonable level of difficulty: A project that would normally require serious thought about architecture and business domain. The project involves a lot of math, and I’ll note upfront—if you know someone in the financial industry, I’d love to verify my data with more powerful banking models! I’d appreciate sharing this post.
Real value: Loan calculators can save people a lot of money.

What does the calculator offer? 📊

Weekly Wellness Intelligence

Tue, 17 Jun 2025 00:00:00 +0000

📊 How My AI Coach Caught What I Missed During a Rough Week

After successfully automating my morning routine, I realized I had a goldmine of data but no systematic way to learn from it. I was tracking 8 different wellness habits daily but only looked at the data when something went wrong.

Then I had one of those weeks. You know the type—where everything feels harder, anxiety creeps in, and you start questioning your systems. But instead of spiraling, my AI-powered weekly performance review revealed something I never would have spotted manually: a breakthrough insight hidden in the struggle.

Czy istnieje wolna wola?

Tue, 21 Oct 2025 00:00:00 +0000

Rozmowa o wolnej woli i determinizmie — o tym, jak świadomość własnego mechanizmu daje spokój, zrozumienie i subtelne poczucie wpływu.

Python

Fri, 17 Oct 2025 00:00:00 +0000

Przegląd

Zebrane poniżej notatki to skrócona ściągawka przygotowująca do rozmów technicznych o Pythonie. Materiał został pogrupowany tak, aby najpierw uporządkować kluczowe pytania rekrutacyjne, następnie odświeżyć nowości w Pythonie 3.14, przypomnieć fundamenty języka oraz zanurzyć się w tematach związanych z równoległością i architekturą CPythona.

Interview Essentials

1. Główne cechy języka Python

Python to interpretowany, wysokopoziomowy język ogólnego przeznaczenia, który stawia na czytelność i produktywność.

Dynamiczne typowanie — typy wartości ustalane są w czasie wykonania.
Interpretowany — brak konieczności kompilacji do kodu maszynowego.
Garbage Collector — automatyczne zarządzanie pamięcią i odzyskiwanie nieużywanych obiektów.
Czysta obiektowość — w Pythonie wszystko jest obiektem, łącznie z funkcjami i klasami.
Rozszerzalność — łatwa integracja z modułami w C/C++ i bogaty ekosystem bibliotek.
Wieloparadygmatowość — programowanie imperatywne, funkcyjne, obiektowe oraz asynchroniczne.
Pythonic style — nacisk na czytelność, idiomy i wytyczne PEP 8.

Na rozmowie warto dodać:

11B Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - 11B

Generated: 2025-09-24 06:24:53

📊 Buy & Hold Benchmark

Total Return: +4482.28%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘2010-10-28 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 5444}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	11B	434.42%	-13.8%	-27.2%	-27.9%	-43.9%	-4047.86%	0.32	-65.80%	72	50.00%	$534,423
dow_theory	11B	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	11B	902.61%	-5.5%	-20.3%	-25.0%	-43.3%	-3579.67%	0.40	-70.09%	66	50.00%	$1,002,613
bollinger_oscillators	11B	233.02%	-17.0%	-14.1%	-46.3%	-51.2%	-4249.26%	0.21	-64.48%	61	49.18%	$333,021
macd_divergence	11B	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	11B	680.09%	-12.7%	-16.9%	-16.9%	-29.0%	-3802.19%	0.32	-84.04%	50	50.00%	$780,091
mean_reversion_multi_tf	11B	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
relative_strength_rotation	11B	945.31%	-12.7%	-16.9%	-16.9%	-29.5%	-3536.96%	0.39	-86.12%	62	50.00%	$1,045,314
gap_trading	11B	1069.60%	-17.0%	-16.2%	-18.4%	-57.3%	-3412.68%	0.35	-74.70%	5	40.00%	$1,169,603
volatility_expansion	11B	886.44%	0.0%	0.0%	0.0%	-15.9%	-3595.84%	0.37	-76.16%	8	50.00%	$986,438
momentum_kirkpatrick	11B	1616.73%	-2.1%	-3.7%	-29.5%	-27.8%	-2865.54%	0.46	-60.90%	170	50.00%	$1,716,734

Best Strategy: momentum_kirkpatrick

Symbol: 11B
Total Return: 1616.73%
Sharpe Ratio: 0.46
Max Drawdown: -60.90%
Final Portfolio Value: $1,716,734

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

AMB Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - AMB

Generated: 2025-09-24 06:20:31

📊 Buy & Hold Benchmark

Total Return: +350.06%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2005-06-22 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 7398}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	AMB	862.81%	-9.2%	-9.2%	-15.9%	-25.4%	512.75%	0.50	-42.24%	96	50.00%	$962,807
dow_theory	AMB	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	AMB	1129.76%	-7.9%	-7.9%	-19.1%	-32.2%	779.70%	0.57	-46.47%	74	50.00%	$1,229,757
bollinger_oscillators	AMB	-47.30%	-4.9%	-6.4%	-8.4%	-19.2%	-397.36%	-0.12	-91.87%	75	49.33%	$52,696
macd_divergence	AMB	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	AMB	514.91%	-7.9%	-7.9%	-14.1%	-26.4%	164.85%	0.37	-51.94%	54	50.00%	$614,908
mean_reversion_multi_tf	AMB	337.55%	0.0%	0.0%	0.0%	-15.0%	-12.51%	0.42	-40.00%	2	50.00%	$437,551
relative_strength_rotation	AMB	1522.07%	-8.1%	-8.1%	-11.2%	-17.9%	1172.02%	0.63	-42.77%	58	50.00%	$1,622,073
gap_trading	AMB	0.87%	0.0%	0.0%	0.0%	0.0%	-349.19%	0.00	-73.94%	6	50.00%	$100,866
volatility_expansion	AMB	-18.47%	0.0%	0.0%	0.0%	0.0%	-368.53%	-0.05	-88.31%	6	50.00%	$81,530
momentum_kirkpatrick	AMB	151.75%	-0.2%	-0.2%	0.8%	-25.6%	-198.30%	0.22	-51.47%	236	50.00%	$251,754

Best Strategy: relative_strength_rotation

Symbol: AMB
Total Return: 1522.07%
Sharpe Ratio: 0.63
Max Drawdown: -42.77%
Final Portfolio Value: $1,622,073

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

ASB Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - ASB

Generated: 2025-09-24 06:46:40

📊 Buy & Hold Benchmark

Total Return: +960.31%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2007-10-30 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 6538}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	ASB	7515.55%	8.4%	-1.2%	39.9%	22.5%	6555.24%	0.75	-59.08%	75	49.33%	$7,615,554
dow_theory	ASB	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	ASB	5744.26%	3.5%	-2.0%	38.8%	18.8%	4783.94%	0.71	-58.06%	70	50.00%	$5,844,257
bollinger_oscillators	ASB	-94.29%	12.0%	46.8%	55.5%	62.0%	-1054.60%	-0.39	-99.02%	71	49.30%	$5,711
macd_divergence	ASB	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	ASB	13743.51%	20.7%	10.5%	56.5%	19.3%	12783.19%	0.79	-72.07%	53	49.06%	$13,843,508
mean_reversion_multi_tf	ASB	2078.74%	20.7%	10.5%	51.0%	14.9%	1118.42%	0.00	0.00%	1	0.00%	$2,178,738
relative_strength_rotation	ASB	16234.14%	5.2%	-15.7%	19.4%	-3.0%	15273.83%	0.83	-70.90%	58	50.00%	$16,334,141
gap_trading	ASB	-3.30%	0.0%	0.0%	0.0%	0.0%	-963.61%	-0.01	-78.17%	8	50.00%	$96,703
volatility_expansion	ASB	533.96%	0.0%	0.0%	0.0%	-0.8%	-426.35%	0.27	-78.75%	8	50.00%	$633,961
momentum_kirkpatrick	ASB	8949.38%	14.5%	-12.8%	8.8%	-15.5%	7989.07%	0.80	-51.92%	213	49.77%	$9,049,384

Best Strategy: relative_strength_rotation

Symbol: ASB
Total Return: 16234.14%
Sharpe Ratio: 0.83
Max Drawdown: -70.90%
Final Portfolio Value: $16,334,141

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

ATR Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - ATR

Generated: 2025-09-24 07:09:50

📊 Buy & Hold Benchmark

Total Return: +965.16%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2008-12-18 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 6123}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	ATR	658.91%	53.1%	139.0%	337.5%	519.6%	-306.25%	0.41	-76.10%	77	49.35%	$758,915
dow_theory	ATR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	ATR	561.18%	53.1%	99.1%	263.0%	414.7%	-403.98%	0.39	-72.82%	71	49.30%	$661,180
bollinger_oscillators	ATR	65.78%	0.0%	15.7%	23.7%	74.2%	-899.38%	0.08	-90.80%	72	50.00%	$165,779
macd_divergence	ATR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	ATR	2968.71%	24.9%	71.1%	196.7%	433.3%	2003.55%	0.60	-57.60%	45	48.89%	$3,068,714
mean_reversion_multi_tf	ATR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
relative_strength_rotation	ATR	1956.54%	24.9%	94.9%	286.5%	625.1%	991.38%	0.55	-67.29%	57	49.12%	$2,056,542
gap_trading	ATR	271.98%	53.1%	139.0%	373.8%	761.6%	-693.19%	0.21	-89.10%	9	44.44%	$371,977
volatility_expansion	ATR	1085.18%	53.1%	139.0%	373.8%	816.2%	120.02%	0.36	-85.51%	11	45.45%	$1,185,181
momentum_kirkpatrick	ATR	546.11%	46.7%	85.2%	146.4%	207.5%	-419.06%	0.34	-66.38%	187	49.73%	$646,106

Best Strategy: breakout_momentum

Symbol: ATR
Total Return: 2968.71%
Sharpe Ratio: 0.60
Max Drawdown: -57.60%
Final Portfolio Value: $3,068,714

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

BLO Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - BLO

Generated: 2025-09-24 07:22:29

📊 Buy & Hold Benchmark

Total Return: +1036.36%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘2011-06-27 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 5202}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	BLO	384.84%	-10.0%	-15.9%	-0.5%	-28.1%	-651.52%	0.26	-85.17%	72	50.00%	$484,844
dow_theory	BLO	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	BLO	168.31%	-10.5%	-21.7%	-1.3%	-30.5%	-868.06%	0.16	-79.97%	72	50.00%	$268,306
bollinger_oscillators	BLO	925.01%	2.7%	16.2%	33.6%	47.6%	-111.36%	0.44	-78.37%	63	49.21%	$1,025,006
macd_divergence	BLO	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	BLO	514.37%	-9.2%	-17.0%	-5.6%	-18.1%	-521.99%	0.30	-65.05%	48	50.00%	$614,371
mean_reversion_multi_tf	BLO	337.46%	0.0%	0.0%	0.0%	0.0%	-698.90%	0.25	-80.43%	2	50.00%	$437,463
relative_strength_rotation	BLO	524.21%	-21.0%	-23.2%	-13.3%	-21.5%	-512.15%	0.31	-68.56%	57	49.12%	$624,209
gap_trading	BLO	178.70%	-15.0%	-17.1%	13.1%	2.5%	-857.66%	0.16	-79.24%	9	44.44%	$278,701
volatility_expansion	BLO	3129.06%	-15.0%	-17.1%	4.8%	-5.0%	2092.70%	0.59	-63.40%	5	40.00%	$3,229,061
momentum_kirkpatrick	BLO	224.38%	-5.8%	-6.3%	4.2%	-24.6%	-811.98%	0.22	-61.12%	174	50.00%	$324,384

Best Strategy: volatility_expansion

Symbol: BLO
Total Return: 3129.06%
Sharpe Ratio: 0.59
Max Drawdown: -63.40%
Final Portfolio Value: $3,229,061

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

CDR Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - CDR

Generated: 2025-09-24 06:15:47

📊 Buy & Hold Benchmark

Total Return: +3155.43%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘1994-08-02 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 11375}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	CDR	2172.56%	-5.2%	-0.1%	17.1%	56.9%	-982.87%	0.28	-96.37%	148	50.00%	$2,272,563
dow_theory	CDR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	CDR	4439.22%	-8.9%	-9.8%	0.6%	31.7%	1283.79%	0.35	-95.96%	134	50.00%	$4,539,220
bollinger_oscillators	CDR	-34.32%	-0.5%	21.8%	47.7%	19.2%	-3189.75%	-0.03	-96.71%	147	49.66%	$65,675
macd_divergence	CDR	1057.87%	-5.9%	14.1%	52.4%	72.2%	-2097.56%	0.00	0.00%	1	0.00%	$1,157,866
breakout_momentum	CDR	8623.78%	-5.4%	14.8%	55.1%	89.7%	5468.35%	0.34	-97.56%	88	50.00%	$8,723,781
mean_reversion_multi_tf	CDR	3804.20%	-0.5%	-0.5%	-0.5%	-0.5%	648.77%	0.30	-88.97%	3	33.33%	$3,904,200
relative_strength_rotation	CDR	15648.35%	-5.9%	-19.7%	-3.7%	18.3%	12492.92%	0.42	-95.87%	111	49.55%	$15,748,350
gap_trading	CDR	-51.52%	0.0%	0.0%	0.0%	0.0%	-3206.95%	-0.07	-90.96%	14	50.00%	$48,477
volatility_expansion	CDR	1949.51%	-5.9%	14.1%	52.4%	72.2%	-1205.92%	0.21	-99.35%	9	44.44%	$2,049,511
momentum_kirkpatrick	CDR	10284.39%	-9.6%	-6.6%	11.4%	24.6%	7128.96%	0.42	-74.43%	367	49.86%	$10,384,389

Best Strategy: relative_strength_rotation

Symbol: CDR
Total Return: 15648.35%
Sharpe Ratio: 0.42
Max Drawdown: -95.87%
Final Portfolio Value: $15,748,350

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

CIG Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - CIG

Generated: 2025-09-24 07:18:10

📊 Buy & Hold Benchmark

Total Return: +161.95%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2007-11-30 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 6507}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	CIG	290.29%	36.4%	68.7%	79.0%	61.6%	128.34%	0.20	-88.14%	89	49.44%	$390,289
dow_theory	CIG	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	CIG	1216.65%	36.4%	68.7%	75.1%	47.4%	1054.70%	0.37	-78.81%	81	49.38%	$1,316,649
bollinger_oscillators	CIG	-62.95%	0.0%	0.0%	14.7%	-71.9%	-224.90%	-0.13	-85.50%	76	50.00%	$37,047
macd_divergence	CIG	108.45%	36.4%	68.7%	95.3%	-52.3%	-53.50%	0.00	0.00%	1	0.00%	$208,451
breakout_momentum	CIG	232.29%	36.4%	68.7%	74.8%	9.6%	70.34%	0.15	-85.64%	75	49.33%	$332,289
mean_reversion_multi_tf	CIG	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
relative_strength_rotation	CIG	640.44%	36.4%	68.7%	62.4%	21.4%	478.49%	0.26	-83.23%	73	49.32%	$740,438
gap_trading	CIG	3.74%	0.0%	0.0%	0.0%	0.0%	-158.21%	0.01	-87.20%	16	50.00%	$103,737
volatility_expansion	CIG	70.27%	36.4%	68.7%	78.5%	-19.4%	-91.67%	0.06	-81.69%	9	44.44%	$170,275
momentum_kirkpatrick	CIG	150.80%	18.5%	37.9%	18.3%	-16.0%	-11.15%	0.14	-78.13%	216	50.00%	$250,800

Best Strategy: volume_confirmation

Symbol: CIG
Total Return: 1216.65%
Sharpe Ratio: 0.37
Max Drawdown: -78.81%
Final Portfolio Value: $1,316,649

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

DNP Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - DNP

Generated: 2025-09-24 07:13:59

📊 Buy & Hold Benchmark

Total Return: +1127.47%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘2017-04-19 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 3079}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	DNP	150.43%	2.0%	-5.8%	21.7%	-14.3%	-977.04%	0.42	-50.37%	38	50.00%	$250,427
dow_theory	DNP	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	DNP	266.70%	0.6%	-5.2%	22.6%	-9.7%	-860.76%	0.60	-43.77%	32	50.00%	$366,702
bollinger_oscillators	DNP	521.13%	-8.4%	-4.1%	42.3%	65.4%	-606.34%	0.95	-21.48%	45	48.89%	$621,127
macd_divergence	DNP	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	DNP	167.24%	-1.9%	-11.3%	3.3%	-12.8%	-960.23%	0.49	-44.52%	28	50.00%	$267,236
mean_reversion_multi_tf	DNP	120.37%	0.0%	0.0%	0.0%	0.0%	-1007.10%	0.52	-20.84%	2	50.00%	$220,372
relative_strength_rotation	DNP	6.37%	-5.5%	-14.6%	-8.2%	-14.2%	-1121.10%	0.03	-45.31%	44	50.00%	$106,372
gap_trading	DNP	-13.22%	0.0%	0.0%	0.0%	0.0%	-1140.69%	-0.16	-29.67%	2	50.00%	$86,779
volatility_expansion	DNP	175.78%	0.0%	0.0%	0.0%	0.0%	-951.69%	0.66	-30.91%	2	50.00%	$275,779
momentum_kirkpatrick	DNP	213.20%	-6.4%	-0.0%	11.0%	23.6%	-914.26%	0.59	-28.39%	104	50.00%	$313,204

Best Strategy: bollinger_oscillators

Symbol: DNP
Total Return: 521.13%
Sharpe Ratio: 0.95
Max Drawdown: -21.48%
Final Portfolio Value: $621,127

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

KTY Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - KTY

Generated: 2025-09-24 07:00:42

📊 Buy & Hold Benchmark

Total Return: +9846.54%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘1996-01-30 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 10829}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	KTY	2797.60%	14.7%	4.1%	4.8%	12.7%	-7048.94%	0.51	-47.81%	135	49.63%	$2,897,597
dow_theory	KTY	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	KTY	1857.63%	14.7%	3.4%	-0.0%	11.4%	-7988.91%	0.44	-57.27%	125	49.60%	$1,957,628
bollinger_oscillators	KTY	95.43%	0.0%	8.2%	12.0%	16.8%	-9751.11%	0.09	-82.44%	120	50.00%	$195,431
macd_divergence	KTY	1490.53%	14.7%	14.3%	27.1%	61.7%	-8356.01%	0.00	0.00%	1	0.00%	$1,590,533
breakout_momentum	KTY	734.78%	0.5%	0.2%	7.6%	36.9%	-9111.77%	0.29	-66.17%	79	49.37%	$834,775
mean_reversion_multi_tf	KTY	2060.46%	14.7%	14.3%	27.1%	61.7%	-7786.08%	0.00	0.00%	1	0.00%	$2,160,461
relative_strength_rotation	KTY	2173.50%	1.3%	-9.5%	-3.1%	15.3%	-7673.04%	0.45	-35.83%	117	49.57%	$2,273,497
gap_trading	KTY	2502.10%	14.7%	14.3%	27.1%	61.7%	-7344.44%	0.42	-76.44%	11	45.45%	$2,602,103
volatility_expansion	KTY	397.04%	14.7%	14.3%	27.1%	61.7%	-9449.50%	0.28	-53.57%	7	42.86%	$497,043
momentum_kirkpatrick	KTY	1006.13%	1.8%	-5.8%	1.3%	20.1%	-8840.41%	0.39	-45.31%	335	49.85%	$1,106,131

Best Strategy: trend_momentum

Symbol: KTY
Total Return: 2797.60%
Sharpe Ratio: 0.51
Max Drawdown: -47.81%
Final Portfolio Value: $2,897,597

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

MBR Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - MBR

Generated: 2025-09-24 06:29:06

📊 Buy & Hold Benchmark

Total Return: +398.85%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2010-06-29 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 5565}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	MBR	461.83%	1.3%	1.3%	11.0%	22.8%	62.98%	0.40	-70.49%	41	48.78%	$561,830
dow_theory	MBR	2857.30%	11.4%	-10.4%	-0.9%	12.6%	2458.46%	0.00	0.00%	1	0.00%	$2,957,301
volume_confirmation	MBR	263.73%	-0.4%	-0.4%	-9.0%	-2.6%	-135.11%	0.28	-64.27%	47	48.94%	$363,731
bollinger_oscillators	MBR	-52.39%	7.7%	-13.4%	8.1%	14.4%	-451.23%	-0.18	-90.60%	36	50.00%	$47,611
macd_divergence	MBR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	MBR	325.07%	2.0%	2.0%	-2.8%	-13.5%	-73.78%	0.32	-78.66%	35	48.57%	$425,068
mean_reversion_multi_tf	MBR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
relative_strength_rotation	MBR	928.82%	2.0%	2.0%	-1.6%	-0.5%	529.98%	0.57	-50.96%	35	48.57%	$1,028,823
gap_trading	MBR	2889.35%	11.4%	-10.4%	-0.9%	12.6%	2490.50%	0.00	0.00%	1	0.00%	$2,989,348
volatility_expansion	MBR	1335.47%	0.0%	0.0%	0.0%	0.0%	936.63%	0.60	-73.36%	2	50.00%	$1,435,474
momentum_kirkpatrick	MBR	282.77%	2.4%	-6.5%	-12.6%	-13.0%	-116.07%	0.34	-75.48%	134	50.00%	$382,772

Best Strategy: gap_trading

Symbol: MBR
Total Return: 2889.35%
Sharpe Ratio: 0.00
Max Drawdown: 0.00%
Final Portfolio Value: $2,989,348

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

MVP Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - MVP

Generated: 2025-09-24 06:51:04

📊 Buy & Hold Benchmark

Total Return: +117.90%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2008-06-19 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 6305}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	MVP	134.73%	25.8%	42.1%	51.1%	32.0%	16.84%	0.18	-70.04%	83	49.40%	$234,735
dow_theory	MVP	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	MVP	65.22%	25.8%	42.1%	51.5%	27.3%	-52.68%	0.12	-71.69%	67	49.25%	$165,219
bollinger_oscillators	MVP	28.76%	0.0%	0.0%	-2.3%	13.1%	-89.14%	0.05	-71.67%	66	50.00%	$128,758
macd_divergence	MVP	154.77%	25.8%	42.1%	38.8%	35.9%	36.87%	0.00	0.00%	1	0.00%	$254,769
breakout_momentum	MVP	88.72%	25.8%	42.1%	22.7%	6.3%	-29.18%	0.13	-83.85%	43	48.84%	$188,722
mean_reversion_multi_tf	MVP	289.95%	25.8%	42.1%	38.8%	35.9%	172.05%	0.00	0.00%	1	0.00%	$389,951
relative_strength_rotation	MVP	177.59%	25.8%	32.0%	31.2%	19.9%	59.69%	0.22	-71.04%	61	49.18%	$277,587
gap_trading	MVP	14.91%	25.8%	42.1%	38.8%	35.9%	-102.99%	0.03	-76.86%	5	40.00%	$114,911
volatility_expansion	MVP	95.13%	25.8%	42.1%	38.8%	35.9%	-22.77%	0.13	-72.56%	5	40.00%	$195,125
momentum_kirkpatrick	MVP	162.18%	18.7%	27.2%	22.0%	29.2%	44.28%	0.23	-70.33%	171	49.71%	$262,179

Best Strategy: mean_reversion_multi_tf

Symbol: MVP
Total Return: 289.95%
Sharpe Ratio: 0.00
Max Drawdown: 0.00%
Final Portfolio Value: $389,951

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

OML Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - OML

Generated: 2025-09-24 07:26:42

📊 Buy & Hold Benchmark

Total Return: +113.08%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2012-08-29 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 4773}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	OML	741.03%	121.6%	93.0%	78.0%	42.2%	627.95%	0.44	-57.87%	35	48.57%	$841,029
dow_theory	OML	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	OML	4190.12%	121.6%	88.0%	73.5%	73.2%	4077.04%	0.75	-59.27%	35	48.57%	$4,290,116
bollinger_oscillators	OML	-84.60%	26.4%	22.1%	28.8%	-50.7%	-197.68%	-0.32	-91.57%	44	50.00%	$15,397
macd_divergence	OML	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	OML	5366.92%	121.6%	93.9%	82.7%	100.4%	5253.84%	0.76	-73.25%	21	47.62%	$5,466,917
mean_reversion_multi_tf	OML	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
relative_strength_rotation	OML	692.37%	85.7%	68.5%	55.2%	71.6%	579.29%	0.37	-77.14%	29	48.28%	$792,370
gap_trading	OML	125.86%	121.6%	94.2%	78.9%	-23.9%	12.78%	0.18	-72.98%	9	44.44%	$225,855
volatility_expansion	OML	1468.66%	121.6%	94.2%	78.9%	-23.9%	1355.58%	0.53	-82.40%	9	44.44%	$1,568,658
momentum_kirkpatrick	OML	3274.50%	113.1%	98.0%	95.3%	125.6%	3161.42%	0.77	-74.85%	97	49.48%	$3,374,499

Best Strategy: breakout_momentum

Symbol: OML
Total Return: 5366.92%
Sharpe Ratio: 0.76
Max Drawdown: -73.25%
Final Portfolio Value: $5,466,917

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

PCR Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - PCR

Generated: 2025-09-24 06:33:16

📊 Buy & Hold Benchmark

Total Return: +445.96%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2014-06-25 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 4108}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	PCR	116.31%	-1.6%	-12.8%	-10.5%	-7.7%	-329.65%	0.35	-42.36%	54	50.00%	$216,314
dow_theory	PCR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	PCR	206.55%	-1.7%	-9.0%	-7.7%	-13.4%	-239.41%	0.51	-43.42%	48	50.00%	$306,545
bollinger_oscillators	PCR	-1.62%	3.5%	8.3%	6.0%	4.5%	-447.58%	-0.01	-63.54%	48	50.00%	$98,377
macd_divergence	PCR	-20.96%	1.9%	-1.6%	-8.1%	-21.0%	-466.92%	0.00	0.00%	1	0.00%	$79,043
breakout_momentum	PCR	364.73%	-4.3%	-11.5%	-15.4%	-16.0%	-81.23%	0.68	-37.95%	35	48.57%	$464,730
mean_reversion_multi_tf	PCR	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
relative_strength_rotation	PCR	326.18%	1.9%	-1.6%	-11.2%	-31.2%	-119.78%	0.63	-52.78%	31	48.39%	$426,178
gap_trading	PCR	60.48%	1.9%	-1.6%	-8.1%	-8.3%	-385.48%	0.00	0.00%	1	0.00%	$160,480
volatility_expansion	PCR	430.60%	0.0%	0.0%	0.0%	-6.2%	-15.36%	0.68	-53.22%	6	50.00%	$530,604
momentum_kirkpatrick	PCR	384.61%	-1.9%	-2.0%	3.7%	1.4%	-61.35%	0.77	-29.19%	110	50.00%	$484,614

Best Strategy: volatility_expansion

Symbol: PCR
Total Return: 430.60%
Sharpe Ratio: 0.68
Max Drawdown: -53.22%
Final Portfolio Value: $530,604

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

PKO Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - PKO

Generated: 2025-09-24 07:05:26

📊 Buy & Hold Benchmark

Total Return: +456.44%
Analysis Period: Medium-term
Date Range: {‘start’: Timestamp(‘2004-11-10 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 7622}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	PKO	78.83%	-2.4%	-17.1%	3.7%	29.8%	-377.60%	0.13	-49.45%	112	50.00%	$178,835
dow_theory	PKO	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	PKO	83.77%	-2.8%	-10.8%	18.2%	45.6%	-372.67%	0.13	-56.82%	98	50.00%	$183,765
bollinger_oscillators	PKO	196.49%	-2.3%	19.4%	19.4%	51.5%	-259.95%	0.22	-56.48%	101	49.50%	$296,491
macd_divergence	PKO	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	PKO	86.19%	0.0%	-16.6%	-1.4%	43.9%	-370.25%	0.13	-64.41%	66	50.00%	$186,190
mean_reversion_multi_tf	PKO	71.36%	8.5%	-3.2%	33.4%	40.3%	-385.08%	0.08	-67.26%	3	33.33%	$171,363
relative_strength_rotation	PKO	80.49%	1.0%	-17.6%	-1.8%	37.3%	-375.95%	0.13	-52.22%	92	50.00%	$180,485
gap_trading	PKO	4.05%	0.0%	0.0%	0.0%	0.0%	-452.39%	0.02	-33.62%	4	50.00%	$104,049
volatility_expansion	PKO	86.22%	8.5%	-3.2%	33.4%	86.2%	-370.22%	0.00	0.00%	1	0.00%	$186,220
momentum_kirkpatrick	PKO	78.51%	4.8%	0.7%	-0.0%	38.6%	-377.93%	0.13	-63.98%	250	50.00%	$178,506

Best Strategy: bollinger_oscillators

Symbol: PKO
Total Return: 196.49%
Sharpe Ratio: 0.22
Max Drawdown: -56.48%
Final Portfolio Value: $296,491

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

RPC Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - RPC

Generated: 2025-09-24 06:37:46

📊 Buy & Hold Benchmark

Total Return: +99.91%
Analysis Period: Short-term
Date Range: {‘start’: Timestamp(‘1997-12-16 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 10143}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	RPC	347.00%	4.1%	17.8%	17.8%	2.4%	247.09%	0.20	-52.59%	132	50.00%	$447,003
dow_theory	RPC	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	RPC	272.44%	4.1%	17.8%	17.8%	0.3%	172.53%	0.17	-58.53%	120	50.00%	$372,441
bollinger_oscillators	RPC	-3.11%	-6.8%	14.9%	1.4%	-31.9%	-103.02%	-0.00	-92.25%	123	49.59%	$96,893
macd_divergence	RPC	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	RPC	288.71%	7.5%	22.7%	20.5%	7.1%	188.80%	0.16	-70.33%	86	50.00%	$388,715
mean_reversion_multi_tf	RPC	38.99%	-3.4%	10.3%	-2.6%	-37.2%	-60.92%	0.00	0.00%	1	0.00%	$138,994
relative_strength_rotation	RPC	255.36%	1.2%	5.3%	5.3%	-13.9%	155.45%	0.16	-57.07%	110	50.00%	$355,361
gap_trading	RPC	-79.93%	-3.4%	10.3%	-2.6%	-37.2%	-179.84%	-0.25	-87.18%	11	45.45%	$20,071
volatility_expansion	RPC	337.19%	-3.4%	10.3%	-2.6%	-37.2%	237.28%	0.21	-63.49%	11	45.45%	$437,189
momentum_kirkpatrick	RPC	-0.33%	11.6%	27.8%	27.8%	8.0%	-100.24%	-0.00	-74.49%	328	50.00%	$99,671

Best Strategy: trend_momentum

Symbol: RPC
Total Return: 347.00%
Sharpe Ratio: 0.20
Max Drawdown: -52.59%
Final Portfolio Value: $447,003

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

SPL Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - SPL

Generated: 2025-09-24 06:55:45

📊 Buy & Hold Benchmark

Total Return: +11167.36%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘1993-06-22 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 11781}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	SPL	900.81%	-4.7%	-10.0%	12.9%	16.1%	-10266.55%	0.25	-65.21%	148	50.00%	$1,000,807
dow_theory	SPL	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	SPL	591.07%	-4.7%	-8.1%	15.3%	20.6%	-10576.29%	0.21	-77.06%	132	50.00%	$691,072
bollinger_oscillators	SPL	300.93%	5.9%	-0.1%	2.8%	44.9%	-10866.43%	0.15	-74.74%	139	49.64%	$400,933
macd_divergence	SPL	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
breakout_momentum	SPL	2813.46%	-1.8%	-29.5%	-14.7%	24.4%	-8353.90%	0.35	-79.02%	82	50.00%	$2,913,461
mean_reversion_multi_tf	SPL	218.28%	3.9%	-10.3%	12.1%	69.9%	-10949.08%	0.14	-79.16%	3	33.33%	$318,280
relative_strength_rotation	SPL	1182.61%	-8.4%	-17.7%	3.7%	22.7%	-9984.75%	0.28	-65.56%	119	49.58%	$1,282,611
gap_trading	SPL	2597.92%	0.0%	0.0%	0.0%	54.4%	-8569.44%	0.40	-58.00%	14	50.00%	$2,697,923
volatility_expansion	SPL	578.90%	0.0%	0.0%	0.0%	0.0%	-10588.46%	0.20	-79.16%	8	50.00%	$678,901
momentum_kirkpatrick	SPL	1450.24%	3.3%	-8.0%	-19.3%	11.2%	-9717.12%	0.32	-55.41%	390	50.00%	$1,550,240

Best Strategy: breakout_momentum

Symbol: SPL
Total Return: 2813.46%
Sharpe Ratio: 0.35
Max Drawdown: -79.02%
Final Portfolio Value: $2,913,461

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

VOX Stock Analysis

Wed, 24 Sep 2025 07:28:47 +0000

Backtest Summary - VOX

Generated: 2025-09-24 06:42:20

📊 Buy & Hold Benchmark

Total Return: +1565.59%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘2011-10-11 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 5096}

This represents the return from buying at the start and holding until the end of the analysis period.

Performance Overview

Strategy	Symbol	Total Return	3M Return	6M Return	12M Return	24M Return	Excess Return	Sharpe Ratio	Max Drawdown	Trades	Win Rate	Final Value
trend_momentum	VOX	1442.30%	13.1%	19.7%	48.9%	146.8%	-123.29%	0.82	-34.15%	56	50.00%	$1,542,300
dow_theory	VOX	0.00%	0.0%	0.0%	0.0%	0.0%	0.00%	0.00	0.00%	0	0.00%	$100,000
volume_confirmation	VOX	1378.62%	13.4%	15.7%	44.0%	135.6%	-186.98%	0.79	-35.78%	45	48.89%	$1,478,618
bollinger_oscillators	VOX	306.52%	1.3%	1.3%	1.3%	12.4%	-1259.07%	0.51	-52.01%	55	49.09%	$406,522
macd_divergence	VOX	1565.59%	13.4%	22.0%	60.4%	222.6%	0.00%	0.00	0.00%	1	0.00%	$1,665,595
breakout_momentum	VOX	594.15%	11.6%	8.4%	35.4%	128.5%	-971.45%	0.56	-50.05%	42	50.00%	$694,149
mean_reversion_multi_tf	VOX	585.16%	13.4%	22.0%	60.4%	222.6%	-980.43%	0.57	-54.23%	3	33.33%	$685,164
relative_strength_rotation	VOX	244.03%	13.4%	4.1%	30.1%	117.8%	-1321.56%	0.35	-54.30%	57	49.12%	$344,034
gap_trading	VOX	1246.04%	13.4%	22.0%	60.4%	222.6%	-319.56%	0.00	0.00%	1	0.00%	$1,346,037
volatility_expansion	VOX	656.23%	13.4%	22.0%	60.4%	222.6%	-909.36%	0.63	-43.59%	9	44.44%	$756,231
momentum_kirkpatrick	VOX	267.12%	8.5%	3.0%	7.2%	62.2%	-1298.47%	0.42	-31.03%	154	50.00%	$367,122

Best Strategy: macd_divergence

Symbol: VOX
Total Return: 1565.59%
Sharpe Ratio: 0.00
Max Drawdown: 0.00%
Final Portfolio Value: $1,665,595

Key Metrics

Initial Capital: $100,000
Analysis Date: 2025-09-24
Portfolio Manager: Active (Extreme returns fix applied)

Period Analysis

This report includes period-based return analysis for the following timeframes:

Analiza Inwestycyjna - Przewodnik dla Początkujących

Tue, 08 Jul 2025 00:00:00 +0000

Ten przewodnik jest przeznaczony dla początkujących inwestorów, którzy chcą nauczyć się systematycznie analizować akcje. Nie wymaga wcześniejszego doświadczenia!

🎯 Czego się Nauczysz

Po przeczytaniu tego przewodnika będziesz rozumieć:

Jak ocenić, czy firma jest zdrowa finansowo
Podstawy analizy technicznej do lepszego wyczucia momentu
Jak oceniać i zarządzać ryzykiem inwestycyjnym
Prosty schemat podejmowania decyzji
Jak to wszystko łączy się z automatycznymi narzędziami analizy

📊 Część 1: Analiza Fundamentalna (Kontrola Zdrowia Firmy)

Analiza fundamentalna to jak badania lekarskie dla firmy. Patrzymy na liczby finansowe, żeby zrozumieć, czy biznes jest silny i rentowny.

Investment Analysis Framework - Beginner's Guide

Tue, 08 Jul 2025 00:00:00 +0000

This guide is designed for beginner investors who want to learn how to analyze stocks systematically. No prior experience required!

🎯 What You’ll Learn

By the end of this guide, you’ll understand:

How to evaluate if a company is financially healthy
Basic technical analysis to time your entries
How to assess and manage investment risk
A simple decision-making framework
How this connects with automated analysis tools

📊 Part 1: Fundamental Analysis (Company Health Check)

Fundamental analysis is like getting a health check-up for a company. We look at financial numbers to understand if the business is strong and profitable.

Investment Resources & Tools - Complete Guide

Tue, 08 Jul 2025 00:00:00 +0000

A comprehensive collection of tools, websites, and resources for beginner investors. All links tested and categorized by usefulness and skill level.

📊 Polish Market Resources (GPW Focus)

🏛️ Official Sources

Warsaw Stock Exchange (GPW)

Website: gpw.pl
What it offers: Official market data, company reports, statistics
Best for: Official announcements, market statistics
Cost: Free

Polish Financial Supervision Authority (KNF)

Website: knf.gov.pl
What it offers: Regulatory information, warnings, investor protection
Best for: Checking if broker is licensed, investor alerts
Cost: Free

📈 Analysis Platforms

BiznesRadar ⭐ Most Important

Zasoby i Narzędzia Inwestycyjne - Kompletny Przewodnik

Tue, 08 Jul 2025 00:00:00 +0000

Kompleksowy zbiór narzędzi, stron internetowych i zasobów dla początkujących inwestorów. Wszystkie linki przetestowane i skategoryzowane według użyteczności i poziomu umiejętności.

📊 Zasoby Polskiego Rynku (GPW)

🏛️ Źródła Oficjalne

Giełda Papierów Wartościowych (GPW)

Strona: gpw.pl
Co oferuje: Oficjalne dane rynkowe, raporty spółek, statystyki
Najlepsze dla: Oficjalne komunikaty, statystyki rynku
Koszt: Darmowe

Komisja Nadzoru Finansowego (KNF)

Strona: knf.gov.pl
Co oferuje: Informacje regulacyjne, ostrzeżenia, ochrona inwestorów
Najlepsze dla: Sprawdzanie licencji brokera, alerty inwestorskie
Koszt: Darmowe

📈 Platformy Analityczne

BiznesRadar ⭐ Najważniejsze

AI TESTING AGENT – ROLE DEFINITION & GUIDELINES

Sat, 05 Jul 2025 00:00:00 +0000

Absolutely. Here’s the generalized, reusable version of the AI Testing Agent role definition. You can plug in any project context by replacing [PROJECT DESCRIPTION], and it’s structured for universal application across projects requiring advanced technical validation:

🎯 ROLE PURPOSE

As the AI Testing Agent, your mission is to validate the functional, architectural, analytical, and usability quality of a system described as:

[PROJECT DESCRIPTION]

You are not a task executor—you are a technical quality engineer with strategic autonomy. You are expected to:

Building Better Habit Visualization

Thu, 26 Jun 2025 00:00:00 +0000

Sometimes the best projects start as experiments, not solutions.

I’ve been accumulating daily habit data for months through my automated morning routine system, but I was only scratching the surface of what that data could tell me. Sure, I had green checkboxes in Notion, but I couldn’t easily see patterns, trends, or understand where I actually stood in my progress.

This became the perfect excuse to practice something I’d been wanting to explore: AI agent orchestration and custom visualization APIs. What started as a technical exercise evolved into something genuinely useful—a visual intelligence layer that transforms raw habit data into clear insights.

Mastering Vibe Coding! A Developer's Guide to AI-Assisted Programming

Mon, 23 Jun 2025 00:00:00 +0000

What is Vibe Coding?

Vibe coding is a term coined by AI legend Andrej Karpathy to describe the fluid, iterative way of programming alongside Large Language Models (LLMs). It’s that magical flow state where you generate code, tweak it, generate more, and make incredible progress by working collaboratively with AI tools like ChatGPT and Claude.

But here’s the thing: while vibe coding is incredibly powerful and fun, it’s easy to get led astray. Without proper technique, you might find yourself with 200 lines of broken code, wondering what went wrong.

Python Syntactic Sugar Cheatsheet

Mon, 23 Jun 2025 00:00:00 +0000

🟢 Core Patterns

List Comprehensions

When to use: Transform or filter a list of items

Basic Pattern:

[expression for item in list]

Simple Examples:

# Transform each number
numbers = [1, 2, 3, 4, 5]
squares = [x * x for x in numbers]
# Result: [1, 4, 9, 16, 25]

# Filter with condition
even_numbers = [x for x in numbers if x % 2 == 0]
# Result: [2, 4]

# Instead of this loop:
result = []
for x in numbers:
 if x % 2 == 0:
 result.append(x * x)

# Write this:
result = [x * x for x in numbers if x % 2 == 0]

Dictionary Comprehensions

When to use: Build dictionaries from existing data

Pydantic + LLM Structured Outputs Cheat Sheet

Sat, 21 Jun 2025 00:00:00 +0000

What is Pydantic?

A Python library for data validation and serialization that automatically:

Validates data types
Converts types when possible
Provides clear error messages
Serializes to/from JSON, dictionaries

Basic Pydantic Model

from pydantic import BaseModel

class Evaluation(BaseModel):
 is_acceptable: bool
 feedback: str

Using with LLMs

The Problem

LLMs return unstructured text
Hard to parse and validate
Inconsistent formats

The Solution

Use Pydantic models to enforce structure!

response = gemini.beta.chat.completions.parse(
 model="gemini-2.0-flash", 
 messages=messages, 
 response_format=Evaluation # Your Pydantic model
)

# Returns validated Evaluation object
print(response.is_acceptable) # bool
print(response.feedback) # str

How It Works Under the Hood

Your Pydantic Model → JSON Schema (automatic conversion)
JSON Schema → LLM API (as instructions)
LLM generates → JSON matching schema
Library parses → Validated Pydantic object

Example JSON Schema Generated

{
 "type": "object",
 "properties": {
 "is_acceptable": {"type": "boolean"},
 "feedback": {"type": "string"}
 },
 "required": ["is_acceptable", "feedback"]
}

Benefits

✅ Guaranteed structure - Always get expected fields
✅ Type safety - Fields are correct types
✅ Error handling - Validation catches format errors
✅ Easy processing - Use data immediately in code
✅ No manual parsing - Library handles conversion

The Culture Map Complete Study Guide

Sat, 21 Jun 2025 00:00:00 +0000

🎯 Core Concept

The Big Idea: Globalization has created unprecedented cultural complexity in the workplace. The Culture Map provides a systematic framework for navigating these differences through cultural relativity - understanding cultures in relation to each other, not as absolutes.

Why It Matters: Germans appear direct compared to Japanese colleagues but indirect compared to Dutch team members. This relative positioning makes cultural navigation practical and actionable.

📊 The 8 Cultural Dimensions

1. Communicating: Low-Context ↔ High-Context

Low-Context (Left): Messages are explicit, clear, and literal High-Context (Right): Messages rely on context, subtlety, and “reading between the lines”

Code Review practices

Tue, 10 Jun 2025 00:00:00 +0000

Introduction

A code review is a process where someone other than the author(s) of a piece of code examines that code.

At Google, we use code review to maintain the quality of our code and products.

This documentation is based on Google’s canonical description of their code review processes and policies.

Guide Structure

This page provides an overview of the code review process. There are two other comprehensive documents that are part of this guide:

Ownership Principles

Tue, 10 Jun 2025 00:00:00 +0000

Everything needs an owner
The owner doesn’t have to be the task executor
Ownership can change (but must be notified to the team)
The owner can have assistants but there is only one owner
Ownership must be actively accepted by the owner - you can’t assign ownership without their knowledge
The owner should actively communicate any changes, progress, or issues to the team and stakeholders
If the owner delegates tasks, they should ensure the delegate understands their responsibilities and has the necessary resources to fulfill them

About Me

Mon, 09 Jun 2025 00:00:00 +0000

Experienced technology leader with over 12 years of hands-on and strategic expertise in software development, engineering leadership, and digital transformation. Proven trackrecord of aligning technical vision with business strategy, scaling cross-functional teams, and delivering high-impact projects across industries. Skilled in full-stack development, cloud-native solutions, and agile program delivery.

N8N Advanced n8n Workflow Examples - Database & API Integration

Mon, 09 Jun 2025 00:00:00 +0000

Advanced n8n Workflow Examples - Database & API Integration

Complex workflows with error handling, data validation, and multi-step processing. These examples demonstrate enterprise-level automation patterns.

1. CRM Data Synchronization with Error Handling

Purpose: Sync external CRM data to local database with comprehensive error handling Use Case: Customer data synchronization, inventory management, data warehousing

{
 "name": "CRM to Database Sync with Error Handling",
 "nodes": [
 {
 "parameters": {
 "rule": {
 "interval": [
 {
 "field": "cronExpression",
 "expression": "0 */4 * * *"
 }
 ]
 }
 },
 "id": "schedule-sync",
 "name": "Every 4 Hours",
 "type": "n8n-nodes-base.scheduleTrigger",
 "typeVersion": 1.2,
 "position": [240, 400]
 },
 {
 "parameters": {
 "method": "GET",
 "url": "https://api.crm-system.com/v2/contacts",
 "authentication": "predefinedCredentialType",
 "nodeCredentialType": "httpHeaderAuth",
 "sendQuery": true,
 "queryParameters": {
 "parameters": [
 {
 "name": "limit",
 "value": "100"
 },
 {
 "name": "updated_since",
 "value": "={{ $workflow.lastSuccessTime || DateTime.now().minus({days: 1}).toISO() }}"
 },
 {
 "name": "include_deleted",
 "value": "true"
 }
 ]
 },
 "options": {
 "pagination": {
 "paginationMode": "updateAParameterInEachRequest",
 "paginationCompleteExpression": "={{ !$response.body.has_more }}",
 "limitPagesFetched": true,
 "maxRequests": 20,
 "pageSize": 100
 },
 "retry": {
 "enabled": true,
 "maxTries": 3,
 "waitBetweenTries": 2000
 },
 "timeout": 15000
 }
 },
 "id": "fetch-crm-data",
 "name": "Fetch CRM Contacts",
 "type": "n8n-nodes-base.httpRequest",
 "typeVersion": 4.2,
 "position": [460, 400],
 "credentials": {
 "httpHeaderAuth": {
 "id": "crm-api-credentials",
 "name": "CRM API Key"
 }
 }
 },
 {
 "parameters": {
 "conditions": {
 "options": {
 "leftValue": "={{ $json.contacts && Array.isArray($json.contacts) && $json.contacts.length > 0 }}",
 "operation": "equal",
 "rightValue": true
 }
 }
 },
 "id": "check-data-exists",
 "name": "Has Contact Data?",
 "type": "n8n-nodes-base.if",
 "typeVersion": 2,
 "position": [680, 400]
 },
 {
 "parameters": {
 "fieldToSplitOut": "contacts",
 "options": {}
 },
 "id": "split-contacts",
 "name": "Split Contact Array",
 "type": "n8n-nodes-base.itemLists",
 "typeVersion": 3.3,
 "position": [900, 320]
 },
 {
 "parameters": {
 "language": "javaScript",
 "jsCode": "// Validate and transform contact data\nconst validContacts = [];\nconst invalidContacts = [];\n\nfor (const item of $input.all()) {\n const contact = item.json;\n const errors = [];\n \n // Validation rules\n if (!contact.email || !/^[^\\s@]+@[^\\s@]+\\.[^\\s@]+$/.test(contact.email)) {\n errors.push('Invalid or missing email');\n }\n \n if (!contact.id || contact.id.toString().length < 1) {\n errors.push('Missing contact ID');\n }\n \n if (!contact.first_name || contact.first_name.trim().length < 1) {\n errors.push('Missing first name');\n }\n \n if (contact.phone && !/^[\\+]?[1-9][\\d\\s\\-\\(\\)]{7,15}$/.test(contact.phone.replace(/\\s/g, ''))) {\n errors.push('Invalid phone number format');\n }\n \n if (errors.length === 0) {\n // Transform valid contact\n const transformedContact = {\n external_id: contact.id.toString(),\n first_name: contact.first_name.trim(),\n last_name: (contact.last_name || '').trim(),\n email: contact.email.toLowerCase().trim(),\n phone: contact.phone ? contact.phone.replace(/[^\\d\\+]/g, '') : null,\n company: (contact.company || '').trim() || null,\n status: contact.status || 'active',\n tags: Array.isArray(contact.tags) ? contact.tags.join(',') : null,\n created_at: contact.created_date || contact.created_at,\n updated_at: contact.updated_date || contact.updated_at,\n is_deleted: contact.is_deleted || false,\n sync_timestamp: new Date().toISOString(),\n source: 'crm_sync'\n };\n \n validContacts.push({ json: transformedContact });\n } else {\n // Log invalid contact\n invalidContacts.push({ \n json: { \n original_contact: contact, \n validation_errors: errors,\n rejected_at: new Date().toISOString()\n } \n });\n \n console.log(`Invalid contact ${contact.id}: ${errors.join(', ')}`);\n }\n}\n\nconsole.log(`Processed ${$input.all().length} contacts: ${validContacts.length} valid, ${invalidContacts.length} invalid`);\n\n// Store invalid contacts for later review\nif (invalidContacts.length > 0) {\n $workflow.staticData.invalidContacts = invalidContacts;\n}\n\nreturn validContacts;"
 },
 "id": "validate-transform",
 "name": "Validate & Transform Data",
 "type": "n8n-nodes-base.code",
 "typeVersion": 2,
 "position": [1120, 320]
 },
 {
 "parameters": {
 "operation": "select",
 "schema": {
 "__rl": true,
 "value": "public",
 "mode": "list",
 "cachedResultName": "public"
 },
 "table": {
 "__rl": true,
 "value": "contacts",
 "mode": "list",
 "cachedResultName": "contacts"
 },
 "where": {
 "values": [
 {
 "column": "external_id",
 "condition": "equal",
 "value": "={{ $json.external_id }}"
 }
 ]
 },
 "options": {}
 },
 "id": "check-contact-exists",
 "name": "Check if Contact Exists",
 "type": "n8n-nodes-base.postgres",
 "typeVersion": 2.4,
 "position": [1340, 320],
 "credentials": {
 "postgres": {
 "id": "postgres-db-connection",
 "name": "PostgreSQL Database"
 }
 }
 },
 {
 "parameters": {
 "conditions": {
 "options": {
 "leftValue": "={{ $('Check if Contact Exists').itemMatching($itemIndex).json }}",
 "operation": "isNotEmpty"
 }
 }
 },
 "id": "contact-exists-check",
 "name": "Contact Exists?",
 "type": "n8n-nodes-base.if",
 "typeVersion": 2,
 "position": [1560, 320]
 },
 {
 "parameters": {
 "operation": "update",
 "schema": {
 "__rl": true,
 "value": "public",
 "mode": "list"
 },
 "table": {
 "__rl": true,
 "value": "contacts",
 "mode": "list"
 },
 "updateKey": "external_id",
 "columns": {
 "mappingMode": "defineBelow",
 "value": {
 "first_name": "={{ $json.first_name }}",
 "last_name": "={{ $json.last_name }}",
 "email": "={{ $json.email }}",
 "phone": "={{ $json.phone }}",
 "company": "={{ $json.company }}",
 "status": "={{ $json.status }}",
 "tags": "={{ $json.tags }}",
 "updated_at": "={{ $json.updated_at }}",
 "sync_timestamp": "={{ $json.sync_timestamp }}",
 "is_deleted": "={{ $json.is_deleted }}"
 }
 },
 "options": {}
 },
 "id": "update-contact",
 "name": "Update Existing Contact",
 "type": "n8n-nodes-base.postgres",
 "typeVersion": 2.4,
 "position": [1780, 220],
 "credentials": {
 "postgres": {
 "id": "postgres-db-connection",
 "name": "PostgreSQL Database"
 }
 }
 },
 {
 "parameters": {
 "operation": "insert",
 "schema": {
 "__rl": true,
 "value": "public",
 "mode": "list"
 },
 "table": {
 "__rl": true,
 "value": "contacts",
 "mode": "list"
 },
 "columns": {
 "mappingMode": "defineBelow",
 "value": {
 "external_id": "={{ $json.external_id }}",
 "first_name": "={{ $json.first_name }}",
 "last_name": "={{ $json.last_name }}",
 "email": "={{ $json.email }}",
 "phone": "={{ $json.phone }}",
 "company": "={{ $json.company }}",
 "status": "={{ $json.status }}",
 "tags": "={{ $json.tags }}",
 "created_at": "={{ $json.created_at }}",
 "updated_at": "={{ $json.updated_at }}",
 "sync_timestamp": "={{ $json.sync_timestamp }}",
 "is_deleted": "={{ $json.is_deleted }}",
 "source": "={{ $json.source }}"
 }
 },
 "options": {
 "ignoreDuplicates": true
 }
 },
 "id": "insert-contact",
 "name": "Insert New Contact",
 "type": "n8n-nodes-base.postgres",
 "typeVersion": 2.4,
 "position": [1780, 420],
 "credentials": {
 "postgres": {
 "id": "postgres-db-connection",
 "name": "PostgreSQL Database"
 }
 }
 },
 {
 "parameters": {
 "mode": "combine",
 "combinationMode": "multiplex",
 "options": {}
 },
 "id": "merge-results",
 "name": "Merge Update/Insert Results",
 "type": "n8n-nodes-base.merge",
 "typeVersion": 2.1,
 "position": [2000, 320]
 },
 {
 "parameters": {
 "language": "javaScript",
 "jsCode": "// Generate sync summary report\nconst allResults = $input.all();\nconst totalProcessed = allResults.length;\n\nconst summary = {\n sync_completed_at: new Date().toISOString(),\n total_contacts_processed: totalProcessed,\n successful_operations: totalProcessed,\n failed_operations: 0,\n invalid_contacts: $workflow.staticData.invalidContacts?.length || 0,\n sync_duration_seconds: Math.round((Date.now() - new Date($('Every 4 Hours').first().json.timestamp || Date.now()).getTime()) / 1000),\n next_sync_scheduled: DateTime.now().plus({ hours: 4 }).toISO()\n};\n\n// Log invalid contacts if any\nif ($workflow.staticData.invalidContacts?.length > 0) {\n console.log('Invalid contacts found:', JSON.stringify($workflow.staticData.invalidContacts, null, 2));\n}\n\nreturn [{ json: summary }];"
 },
 "id": "generate-summary",
 "name": "Generate Sync Summary",
 "type": "n8n-nodes-base.code",
 "typeVersion": 2,
 "position": [2220, 320]
 },
 {
 "parameters": {
 "resource": "message",
 "operation": "post",
 "channel": "#data-sync",
 "text": "✅ CRM Sync Completed Successfully\n\n📊 *Sync Summary:*\n• Total Contacts: {{ $json.total_contacts_processed }}\n• Successful: {{ $json.successful_operations }}\n• Failed: {{ $json.failed_operations }}\n• Invalid: {{ $json.invalid_contacts }}\n• Duration: {{ $json.sync_duration_seconds }}s\n• Next Sync: {{ DateTime.fromISO($json.next_sync_scheduled).toLocaleString() }}\n\n🕐 Completed: {{ DateTime.fromISO($json.sync_completed_at).toLocaleString() }}",
 "otherOptions": {
 "username": "CRM Sync Bot",
 "icon_emoji": ":arrows_counterclockwise:"
 }
 },
 "id": "success-notification",
 "name": "Send Success Notification",
 "type": "n8n-nodes-base.slack",
 "typeVersion": 2.1,
 "position": [2440, 320],
 "credentials": {
 "slackApi": {
 "id": "slack-api-credentials",
 "name": "Slack API Token"
 }
 }
 },
 {
 "parameters": {
 "resource": "message",
 "operation": "post",
 "channel": "#alerts",
 "text": "❌ CRM Sync Failed\n\n*Error Details:*\n• Workflow: {{ $workflow.name }}\n• Error: {{ $json.error?.message || 'Unknown error' }}\n• Time: {{ new Date().toISOString() }}\n• Node: {{ $json.error?.node || 'Unknown' }}\n\n*Stack Trace:*\n```{{ $json.error?.stack || 'No stack trace available' }}```",
 "otherOptions": {
 "username": "Error Bot",
 "icon_emoji": ":x:"
 }
 },
 "id": "error-notification",
 "name": "Send Error Alert",
 "type": "n8n-nodes-base.slack",
 "typeVersion": 2.1,
 "position": [900, 580],
 "credentials": {
 "slackApi": {
 "id": "slack-api-credentials",
 "name": "Slack API Token"
 }
 }
 }
 ],
 "connections": {
 "Every 4 Hours": {
 "main": [
 []
 ]
 },
 "Split Contact Array": {
 "main": [
 [
 {
 "node": "Validate & Transform Data",
 "type": "main",
 "index": 0
 }
 ]
 ]
 },
 "Validate & Transform Data": {
 "main": [
 [
 {
 "node": "Check if Contact Exists",
 "type": "main",
 "index": 0
 }
 ]
 ]
 },
 "Check if Contact Exists": {
 "main": [
 [
 {
 "node": "Contact Exists?",
 "type": "main",
 "index": 0
 }
 ]
 ]
 },
 "Contact Exists?": {
 "main": [
 [
 {
 "node": "Update Existing Contact",
 "type": "main",
 "index": 0
 }
 ],
 [
 {
 "node": "Insert New Contact",
 "type": "main",
 "index": 0
 }
 ]
 ]
 },
 "Update Existing Contact": {
 "main": [
 [
 {
 "node": "Merge Update/Insert Results",
 "type": "main",
 "index": 0
 }
 ]
 ]
 },
 "Insert New Contact": {
 "main": [
 [
 {
 "node": "Merge Update/Insert Results",
 "type": "main",
 "index": 1
 }
 ]
 ]
 },
 "Merge Update/Insert Results": {
 "main": [
 [
 {
 "node": "Generate Sync Summary",
 "type": "main",
 "index": 0
 }
 ]
 ]
 },
 "Generate Sync Summary": {
 "main": [
 [
 {
 "node": "Send Success Notification",
 "type": "main",
 "index": 0
 }
 ]
 ]
 }
 },
 "active": false,
 "settings": {
 "executionOrder": "v1",
 "saveDataErrorExecution": "all",
 "saveDataSuccessExecution": "all",
 "saveManualExecutions": true,
 "callerPolicy": "workflowsFromSameOwner",
 "errorWorkflow": {
 "id": "error-handler-workflow"
 }
 },
 "versionId": "crm-sync-advanced-v1",
 "meta": {
 "templateCredsSetupCompleted": true
 },
 "id": "crm-database-sync-advanced",
 "tags": ["crm", "database", "synchronization", "error-handling", "validation"]
}

2. Multi-Step Order Processing Pipeline

Purpose: Complex order processing with inventory checks, payment processing, and fulfillment Use Case: E-commerce order management, supply chain automation

N8N Developer Cheatsheet

Mon, 09 Jun 2025 00:00:00 +0000

n8n Developer Cheatsheet

A comprehensive reference guide for building n8n workflows based on official documentation from docs.n8n.io.

Node Architecture Fundamentals

Data Flow Model

All data in n8n flows as arrays of JSON objects with this structure:

[
 { "json": { "key1": "value1", "key2": "value2" } },
 { "json": { "key1": "value3", "key2": "value4" } }
]

Key Requirements:

Data must be an array of objects
Each object requires a json key wrapping actual data
From v0.166.0+, n8n auto-adds json key for Function/Code nodes
Custom nodes must ensure proper structure

Node Types

Trigger Nodes (⚡ icon)

N8N Simple n8n workflow examples

Mon, 09 Jun 2025 00:00:00 +0000

Simple n8n Workflow Examples - Ready to Import JSON

Complete, working JSON examples for basic n8n workflows. Copy and import these directly into your n8n instance.

1. Webhook to Slack Notification

Purpose: Receive webhook data and send notification to Slack Use Case: Simple alert system, form submissions, external service notifications

{
 "name": "Webhook to Slack Alert",
 "nodes": [
 {
 "parameters": {
 "httpMethod": "POST",
 "path": "webhook-alert",
 "options": {}
 },
 "id": "webhook-node",
 "name": "Webhook",
 "type": "n8n-nodes-base.webhook",
 "typeVersion": 1.1,
 "position": [240, 300],
 "webhookId": "12345678-1234-1234-1234-123456789abc"
 },
 {
 "parameters": {
 "resource": "message",
 "operation": "post",
 "channel": "#alerts",
 "text": "🚨 New Alert Received!\n\n*Message:* {{ $json.message || 'No message provided' }}\n*Source:* {{ $json.source || 'Unknown' }}\n*Time:* {{ new Date().toLocaleString() }}",
 "otherOptions": {
 "username": "Alert Bot",
 "icon_emoji": ":warning:"
 }
 },
 "id": "slack-node",
 "name": "Send Slack Message",
 "type": "n8n-nodes-base.slack",
 "typeVersion": 2.1,
 "position": [460, 300],
 "credentials": {
 "slackApi": {
 "id": "slack-credentials",
 "name": "Slack API Credentials"
 }
 }
 }
 ],
 "connections": {
 "Webhook": {
 "main": [
 [
 {
 "node": "Send Slack Message",
 "type": "main",
 "index": 0
 }
 ]
 ]
 }
 },
 "active": false,
 "settings": {
 "executionOrder": "v1"
 },
 "versionId": "simple-webhook-slack-v1",
 "meta": {
 "templateCredsSetupCompleted": true
 },
 "id": "webhook-slack-simple",
 "tags": ["webhook", "slack", "alerts", "simple"]
}

2. Daily API Data Fetch

Purpose: Schedule daily API calls to fetch and process data Use Case: Daily reports, data synchronization, monitoring

Machine Learning

Tue, 18 Jan 2022 00:00:00 +0000

Machine Learning Basics

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T as measured by P, improves with experience E

E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game

Algorithms

Supervised Learning We give the algorithm ‘right answer’ to learn
Unsupervised learning Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables. We can derive this structure by clustering the data based on relationships among the variables in the data. With unsupervised learning there is no feedback based on the prediction results.

Problems

Regression: Predict continuous valued output
Classification: Discrete valued output (0,1,…can be more options than two)

Model & Cost Function

To describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h : X → Y so that h(x) is a “good” predictor for the corresponding value of y. For historical reasons, this function h is called a hypothesis. Seen pictorially, the process is therefore like this: