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@alforattvnet: افْتَحِ اللَّهُمَّ لَنَا مَصَارِيعَ الصَّبَاحِ بِمَفَاتِيحِ الرَّحْمَةِ وَالْفَلاحِ #الفرات_صوت_عراقي

قناة الفرات - Alforat TV
قناة الفرات - Alforat TV
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Tuesday 18 August 2026 03:52:31 GMT
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k9___0
𝐊𝐑 :
الله يوفقك مدير القناه يارب
2026-08-18 04:27:04
0
user5571128468641
إمامة علي :
🌹🌹🌹
2026-08-18 05:52:32
0
wi19855
الكاثودي :
❤️❤️❤️
2026-08-18 08:51:51
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s_sccs60
سجاد نجم :
🥰
2026-08-18 04:20:05
0
a10_h08
𝓗𝓪𝓼𝓼𝓸𝓾𝓷☘️✨ :
❤️❤️❤️
2026-08-18 03:59:01
0
almohraa_4
. :
ياالله ياالله ياالله ياالله ياالله ياالله ياالله ياالله
2026-08-18 05:28:49
0
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Recently, I implemented Justin Werlein’s Forever Model programmatically. I was surprised by its performance — the strategy produced a +41.88% return across 1,028 trades over a ~2.5-year backtest, with a max drawdown of just 13.28%. While it failed to outperform the buy-and-hold benchmark (which returned +86.43% in the same period), it maintained relatively consistent equity growth and avoided major volatility events. The win rate was 52.43%, with an average P&L of $40.74 per trade and a profit factor of 1.216 — meaning total gains were only slightly higher than total losses. Risk-adjusted metrics like the Sharpe ratio (0.37) and Sortino ratio (0.745) indicate that the strategy’s returns are modest when accounting for volatility and downside risk. The average winning trade was $438.01 vs. an average loss of $398.79, suggesting only a slight edge per trade. What stood out to me was how stable the model was — no margin calls, controlled drawdowns, and reasonably balanced long/short performance. That said, the edge is thin. This strategy would need optimization and possibly trade filtering to be viable in a live environment with slippage and fees. On my YouTube, I broke down the basic steps of converting a trade idea into a programmatic strategy, and I’ll continue to do so in more depth in coming videos — showing both successes and failures honestly. Comment which traders or strategies you want to see tested next.
Recently, I implemented Justin Werlein’s Forever Model programmatically. I was surprised by its performance — the strategy produced a +41.88% return across 1,028 trades over a ~2.5-year backtest, with a max drawdown of just 13.28%. While it failed to outperform the buy-and-hold benchmark (which returned +86.43% in the same period), it maintained relatively consistent equity growth and avoided major volatility events. The win rate was 52.43%, with an average P&L of $40.74 per trade and a profit factor of 1.216 — meaning total gains were only slightly higher than total losses. Risk-adjusted metrics like the Sharpe ratio (0.37) and Sortino ratio (0.745) indicate that the strategy’s returns are modest when accounting for volatility and downside risk. The average winning trade was $438.01 vs. an average loss of $398.79, suggesting only a slight edge per trade. What stood out to me was how stable the model was — no margin calls, controlled drawdowns, and reasonably balanced long/short performance. That said, the edge is thin. This strategy would need optimization and possibly trade filtering to be viable in a live environment with slippage and fees. On my YouTube, I broke down the basic steps of converting a trade idea into a programmatic strategy, and I’ll continue to do so in more depth in coming videos — showing both successes and failures honestly. Comment which traders or strategies you want to see tested next.
#BOH #NAD #nadskincare #antiagingskincare #over40skincare
#BOH #NAD #nadskincare #antiagingskincare #over40skincare

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