Evidence ke shabd: hit rate, expectancy, drawdown aur base rate.

In 6 shabdon ko ek doosre ka substitute maan lena sabse aam galti hai. Hit rate ginti ka sawaal hai, expectancy paise ka, drawdown raaste ka, aur base rate tulna ka. Ek number ko doosre ki jagah rakh dena kisi dawe ko mazboot nahi banata — woh sirf sawaal badal deta hai.

Isliye kisi bhi dawe ko padhte waqt pehle yeh poochna kaam ka hota hai ki yeh number kis sawaal ka jawab de raha hai, aur kya wahi sawaal tha jo aap poochh rahe the. Aksar nahi hota, aur yeh farak dikhte hi baaki poora dawa alag padha jaane lagta hai.

Tay kiye gaye successful outcomes ko eligible outcomes se baant kar. 100 resolved observations mein 60 defined successes hon toh hit rate 60% hai.

Yeh kya nahi batata: profitability, average payoff, risk ya future success ki probability. Chhoti jeetein aur badi haarein high hit rate ke baawajood negative result de sakti hain.

Aur hit rate ki poori definition uske denominator mein chhupi hoti hai: kaunse observations eligible maane gaye, kaunse chhod diye gaye, aur success ki definition kya thi. Yeh 3 faisle number ko dono taraf kaafi hila sakte hain.

Tay ki gayi assumptions ke under per trade average expected result. Jeetne ki probability, payoff aur cost — yeh 3 saath aate hain.

Yeh kya nahi batata: expected value agle trade ka result nahi hai. Agar inputs estimated ya unstable hain toh output reliable forecast nahi hai, aur ek average distribution, drawdown aur correlated outcomes ko chhupa deta hai.

Tay ki gayi value ka pichhle peak se girna. Peak ka reference aur valuation ka basis saaf hona chahiye.

Yeh kya nahi batata: purana maximum drawdown future ke maximum nuksan ki limit nahi hai. Aur agar deposits aur withdrawals consistently handle na ho toh yeh metric bigad jaata hai.

Yeh bhi maayne rakhta hai ki naapne ki frequency kya thi. Rozana closing values par nikala gaya drawdown aur har pal par nikala gaya drawdown alag numbers dete hain, aur dono ko ek hi cheez samajh lena aam galti hai.

Comparison sample mein kisi tay kiye gaye outcome ki general frequency. Yahi woh benchmark hai jiske bina koi condition-specific number adhoora hai.

Yeh kya nahi batata: alag period ya alag tarike se chuna gaya universe ek unfair baseline ho sakta hai. Sample size, independence, outcome ki definition aur data quality — sab matter karte hain, aur baseline comparison akela causation prove nahi karta.

Shuru aur ant ki value ko ek steady saalana equivalent rate mein badalna. 2 saal mein 10,000 se 12,100 — saalana 10%.

Yeh kya nahi batata: CAGR raasta poori tarah chhupa deta hai, isliye woh volatility ya drawdown ke baare mein kuch nahi kehta. Aur beech mein paisa aata-jaata ho toh yeh galat measure hai.

Alag-alag tareekhon par hue cash flows ka annualised rate, jisme har flow apni date se gina jaata hai. Recurring contributions wala case theek isi ke liye hai.

Yeh kya nahi batata: ise dono signs ke flows chahiye, aur ajeeb patterns mein ek se zyada mathematical jawab ho sakte hain ya jawab converge hi na kare. Solver ka error zero return nahi hota — woh jawab na milne ka signal hai.

Aur XIRR bhi wahi seema rakhta hai jo CAGR rakhta hai: woh ek single rate hai jo poore daur ko chikna kar deta hai. Beech mein kitna utaar-chadhav aaya, uska usme koi zikr nahi hota.

Har number apni definitions par tika hai: outcome kaise define hua, sample kaise chuna gaya, period kaunsa tha, aur data kitna saaf tha. In shartein ko bataye bina koi bhi number utna hi mazboot lagta hai jitna use dikhaya jaaye — aur yahi wajah hai ki summary aksar poore hisaab se zyada bharosemand lagti hai.

Aur inme se koi bhi causation prove nahi karta. Base rate se aage nikalna itna hi batata hai ki tulna mein farak hai. Woh farak kyun hai — yeh ek alag sawaal hai, aur in 6 numbers mein se kisi ke paas uska jawab nahi hai.