{"id":322,"date":"2024-06-25T12:06:37","date_gmt":"2024-06-25T12:06:37","guid":{"rendered":"http:\/\/localhost\/ai-algos\/?page_id=322"},"modified":"2024-07-02T04:59:29","modified_gmt":"2024-07-02T04:59:29","slug":"use-cases","status":"publish","type":"page","link":"https:\/\/uat.crayoninfotech.com\/ai-algos\/use-cases\/","title":{"rendered":"Use Cases"},"content":{"rendered":"\n
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Use Cases<\/h2>\n
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  1. Home<\/a><\/li>\n
  2. Use Cases<\/li>\n <\/ol>\n <\/div>\n<\/div>\n\n
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    \n Traders <\/span\n >Multiplier<\/span>\n <\/h4>\n\n
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    \n Work-out HISTORY<\/span>\n <\/h5>\n <\/div>\n\n
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    \n \n \n \"\"\n \n
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    \n \n <\/path>\n <\/svg>\n <\/div>\n <\/div>\n 20<\/span>\n <\/div>\n <\/div>\n <\/div>\n
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    \n Start with 0.25 million<\/strong> back tests on your\n Risk,Money,Capital decisions; add more.\n <\/p>\n\n

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    \n \n
  3. \n \"\"\n <\/li>\n
  4. \n \"\"\n <\/li>\n
  5. \n \"\"\n <\/li>\n <\/ul>\n <\/div>\n
    \n
      \n
    • \n

      \n Generate Better Odds\n <\/p>\n <\/li>\n

    • \n

      \n Catapult Your risk reward\n <\/p>\n <\/li>\n

    • \n

      Better Your Best<\/p>\n <\/li>\n <\/ul>\n <\/div>\n <\/div>\n\n

      \n
      \n

      Banks<\/h3>\n

      Use Cases 4-9 ,18,19<\/p>\n <\/div>\n\n

      \n Take absolute charge of your risk-rewards on Trading Hedging\n Investment decisions.\n <\/p>\n <\/div>\n\n

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      \n

      Corporates<\/h3>\n

      Use Cases 9, 13-16, 19<\/p>\n <\/div>\n\n \n pan_tool_alt <\/span>\n <\/div>\n\n

      \n Markets is-not your core business; AI can instituionalize best\n practises with zero manpower in execution of ‘better safe than\n sorry.’ mandate.\n <\/p>\n\n

      \n (a) Systematic Hedges can reduce\n funding costs<\/strong> dynamics a lot\n <\/p>\n\n

      \n (b) Learning engines can improve\n Investment returns<\/strong> for lower risk\n <\/p>\n <\/div>\n\n

      \n
      \n

      Funds<\/h3>\n

      Use Cases 1-3, 7-12, 17-20<\/p>\n <\/div>\n\n

      \n Better your Best in portfolio decisions. Beat your Efficient\n Frontier with learning engines for capital allocation\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n

      \n \n
    • \n \n Banks\n <\/button>\n <\/li>\n
    • \n \n Corporates\n <\/button>\n <\/li>\n
    • \n \n Funds\n <\/button>\n <\/li>\n <\/ul>\n\n
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      \n

      USD<\/span>INR<\/h2>\n

      \n Risk-Rewards –\n Strategy<\/span> beats\n Market<\/span>\n <\/h4>\n <\/div>\n
      \n
      \n

      USDINR<\/h4>\n

      \n Risk-Rewards – Strategy beats Market\n <\/h4>\n \"\"\n\n \"\"\n\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Risk<\/h2>\n

      MANAGEMENT<\/h4>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n What are (1) comparative NAVS Market vs\n Strategies(2)Odds(5-95) of Annual Returns (3) Factory Tops\n of 100 K for last Full History, 3 Years, 1 Year\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Generate Better Odds<\/h5>\n <\/div>\n\n

      \n Get Classified Factory-Tops of 100K+ Strategies Selection\n ready for machine-lenses.Strategy pendulum has better odds\n than Market<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n DD<\/th>\n G\/L<\/th>\n <\/tr>\n
      542<\/td>\n -7<\/td>\n 1.8<\/td>\n <\/tr>\n
      62<\/td>\n -42<\/td>\n 0.9<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n USDINR May92-Apr21<\/span>\n Longs made 62 Strategy 542<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      USD<\/span>INR<\/h2>\n

      \n Alpha betters Your\n Strategy<\/span> Risk-Rewards<\/span\n >\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      USDINR<\/h4>\n

      \n Alpha betters Your Strategy Risk- Rewards\n <\/h4>\n \"\"\n\n \"\"\n\n\n
      \n α – You<\/span>\n
      \n
      <\/div>\n
      <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Money<\/h2>\n

      MANAGEMENT<\/h4>\n Strategy gets Alpha<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n WITHOUT<\/span> changing my strategy\n can alpha improve its risk reward\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Catapult Your risk-reward<\/h5>\n <\/div>\n\n

      \n I trade My-Favorite-Strategy. Can-AI improve the\n risk-rewards of my strategy without changing it?\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      P&L<\/th>\n Loss<\/th>\n G\/L<\/th>\n <\/tr>\n
      55<\/td>\n 43<\/td>\n 2.3<\/td>\n <\/tr>\n
      54<\/td>\n 74<\/td>\n 1.7<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n USDINR May92-Apr21<\/span>\n Alpha delivers 55\/43 on my same strategy 54\/74 without\n changing it<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
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      \n
      \n
      \n

      G3<\/span>-INR<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n

      G3-INR<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n DD<\/th>\n <\/tr>\n
      A LEARN<\/td>\n 225<\/td>\n 23<\/td>\n <\/tr>\n
      EUR L<\/td>\n 68<\/td>\n 48<\/td>\n <\/tr>\n
      EUR S<\/td>\n 133<\/td>\n 30<\/td>\n <\/tr>\n
      GBP L<\/td>\n 73<\/td>\n 47<\/td>\n <\/tr>\n
      GBP S<\/td>\n 121<\/td>\n 27<\/td>\n <\/tr>\n
      USD L<\/td>\n 87<\/td>\n 31<\/td>\n <\/tr>\n
      USD S<\/td>\n 108<\/td>\n 26<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 225<\/span>\n <\/div>\n
      \n 133<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between G3 INR Long\/Short portfolio\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 225<\/td>\n 6.0<\/td>\n -15<\/td>\n 34<\/td>\n <\/tr>\n
      EUR\/S<\/td>\n 113<\/td>\n 2.1<\/td>\n -18<\/td>\n 25<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n G3-INR Jan10-Nov23<\/span>\n 6 Pairs Best Return EURINR Shorts 133 A Learning made\n 225<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      GOI<\/span>-10Y<\/h2>\n

      \n Risk-Rewards –\n Strategy<\/span> beats\n Market<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      GOI-10Y<\/h4>\n

      \n Risk-Rewards – Strategy beats Market\n <\/h4>\n \"\"\n\n \"\"\n\n\n \n
      \n Market<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Risk<\/h2>\n

      MANAGEMENT<\/h4>\n Trader gets Strategies<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n What are (1) comparative NAVS Market vs\n Strategies(2)Odds(5-95) of Annual Returns (3) Factory Tops\n of 100 K for last Full History, 3 Years, 1 Year\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Generate Better Odds<\/h5>\n <\/div>\n\n

      \n Get Classified Factory-Tops of 100K+ Strategies Selection\n ready for machine-lenses. Strategy pendulum has better odds\n than Market<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n DD<\/th>\n G\/L<\/th>\n <\/tr>\n
      741<\/td>\n -2<\/td>\n 5.5<\/td>\n <\/tr>\n
      595<\/td>\n -14<\/td>\n 1.3<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n GOI10Y Jan00-Apr21<\/span>\n Longs made 595\/14 Drawdown. Strategy 741\/2 drawdown<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      GOI<\/span>-10Y<\/h2>\n

      \n Alpha betters Your\n Strategy<\/span> Risk-Rewards<\/span\n >\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      GOI-10Y<\/h4>\n

      \n Alpha betters Your Strategy Risk-Rewards\n <\/h4>\n \"\"\n\n \"\"\n\n\n
      \n α – You<\/span>\n
      \n
      <\/div>\n
      <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Money<\/h2>\n

      MANAGEMENT<\/h4>\n Strategy gets Alpha<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n WITHOUT<\/span> changing my strategy\n can alpha improve its risk reward\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Catapult Your risk-reward<\/h5>\n <\/div>\n\n

      \n I trade My-Favorite-Strategy. Can-AI improve the\n risk-rewards of my strategy without changing it?\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      P&L<\/th>\n Loss<\/th>\n G\/L<\/th>\n <\/tr>\n
      1233<\/td>\n 412<\/td>\n 4.0<\/td>\n <\/tr>\n
      1412<\/td>\n 1756<\/td>\n 1.8<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n NIF FUT Jan00-Apr21<\/span\n >\n Alpha-losses better than Strategy<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      GOI<\/span>-10Y<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      GOI-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n
      \n \n
      \n LEARN<\/span\n >\n 956<\/span\n >\n <\/div>\n <\/div>\n \n \n
      \n GOI 10Y<\/span>\n 595<\/span>\n <\/div>\n <\/div>\n \n
      \n CALL<\/span>\n 441<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n
      \n 956\/07<\/span><\/span\n >\n <\/div>\n
      \n 664\/14<\/span><\/span\n >\n <\/div>\n
      \n 441\/00<\/span><\/span\n >\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Call & Bond portfolio.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning Total >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 956<\/td>\n 9.35<\/td>\n -2<\/td>\n 32<\/td>\n <\/tr>\n
      GOI10Y<\/td>\n 664<\/td>\n 7.85<\/td>\n -12<\/td>\n 34<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n GOI10Y-Call Jan00-Feb 24<\/span\n >\n Bond return 664,Call Money 441. Learning made 956 with\n Half the Risk.<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      Equities<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      Equities<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n DD<\/th>\n <\/tr>\n
      A LEARN<\/td>\n 225<\/td>\n 23<\/td>\n <\/tr>\n
      EUR L<\/td>\n 68<\/td>\n 48<\/td>\n <\/tr>\n
      EUR S<\/td>\n 133<\/td>\n 30<\/td>\n <\/tr>\n
      GBP L<\/td>\n 73<\/td>\n 47<\/td>\n <\/tr>\n
      GBP S<\/td>\n 121<\/td>\n 27<\/td>\n <\/tr>\n
      USD L<\/td>\n 87<\/td>\n 31<\/td>\n <\/tr>\n
      USD S<\/td>\n 108<\/td>\n 26<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 225<\/span>\n <\/div>\n
      \n 133<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between G3 INR Long\/Short portfolio\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 225<\/td>\n 6.0<\/td>\n -15<\/td>\n 34<\/td>\n <\/tr>\n
      EUR\/S<\/td>\n 113<\/td>\n 2.1<\/td>\n -18<\/td>\n 25<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n G3-INR Jan10-Nov23<\/span>\n 6 Pairs Best Return EURINR Shorts 133 A Learning made\n 225<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      ASIA<\/span>-10Y<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n USD Terms- NAV & Returns % p.a.<\/b>\n <\/div>\n\n
      \n
      \n \n

      ASIA-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n
      A LEARN<\/td>\n 454<\/td>\n 14<\/td>\n 7.22%<\/td>\n <\/tr>\n
      KOR<\/td>\n 284<\/td>\n 22<\/td>\n 4.93%<\/td>\n <\/tr>\n
      IND<\/td>\n 241<\/td>\n 26<\/td>\n 4.12%<\/td>\n <\/tr>\n
      MAL<\/td>\n 225<\/td>\n 12<\/td>\n 3.81%<\/td>\n <\/tr>\n
      AVG<\/td>\n 250<\/td>\n 20<\/td>\n 4.29%<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 454
      a Learn<\/span\n >\n <\/div>\n
      \n KOR<\/span>\n <\/div>\n
      \n IND<\/span>\n <\/div>\n
      \n MAL<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Asia 10Y Govt bond Returns in USD terms.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 454<\/td>\n 7.22<\/td>\n -14<\/td>\n 24<\/td>\n <\/tr>\n
      KOR10Y<\/td>\n 284<\/td>\n 4.93<\/td>\n -14<\/td>\n 28<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n Asia Gov 10Y Jan02-Sep23 $ Returns<\/span\n >\n KOR Best 284 MAL 225 IND 241. A Learning made 454<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n <\/div>\n <\/div>\n \n \n
      \n
      \n
      \n
      \n

      GOI<\/span>-10Y<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n

      GOI-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n
      \n \n
      \n LEARN<\/span\n >\n 956<\/span\n >\n <\/div>\n <\/div>\n \n \n
      \n GOI 10Y<\/span>\n 595<\/span>\n <\/div>\n <\/div>\n \n
      \n CALL<\/span>\n 441<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n
      \n 956\/07<\/span><\/span\n >\n <\/div>\n
      \n 664\/14<\/span><\/span\n >\n <\/div>\n
      \n 441\/00<\/span><\/span\n >\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Call & Bond portfolio.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning Total >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 956<\/td>\n 9.35<\/td>\n -2<\/td>\n 32<\/td>\n <\/tr>\n
      GOI10Y<\/td>\n 664<\/td>\n 7.85<\/td>\n -12<\/td>\n 34<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n GOI10Y-Call Jan00-Feb 24<\/span\n >\n Bond return 664,Call Money 441. Learning made 956 with\n Half the Risk.<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      Importer<\/h2>\n

      \n Risk-Rewards –\n Strategy<\/span> beats\n Passives<\/span>\n <\/h4>\n <\/div>\n
      \n
      \n \n

      Importer<\/h4>\n

      \n Risk-Rewards – Strategy beats Passives\n <\/h4>\n \"\"\n\n \"\"\n\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Risk<\/h2>\n

      MANAGEMENT<\/h4>\n Hedger gets Strategies<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n What are (1) comparative NAVS Passive vs\n Strategies(2)Odds(Hi-Lo-Avg) of Annual Returns (3) Hedge\n Costs – and Alpha.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Better Safe than Sorry<\/h5>\n Philosophy is ageless. Best practices are-not.<\/small\n >\n <\/div>\n\n

      \n Up-tier Hedging best practices with Strategies to drive your\n hedge\/de-hedge decisions and do far better than traditional\n hedges.\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      STR<\/td>\n 208<\/td>\n 2.6<\/td>\n -4<\/td>\n 26<\/td>\n <\/tr>\n
      ALL<\/td>\n 62<\/td>\n -1.6<\/td>\n -16<\/td>\n 18<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      Importer<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      Importer<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Always Long vs No Hedge.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 149<\/td>\n +1.35<\/td>\n -6<\/td>\n 15<\/td>\n <\/tr>\n
      Passive<\/td>\n 67<\/td>\n -1.35<\/td>\n -18<\/td>\n 18<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n USDINR Longs 1992-Nov 2023<\/span\n >\n A Learning(149) beats Passive (67)<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      Exporters<\/h2>\n

      \n Risk-Rewards –\n Strategy<\/span> beats\n Passives<\/span>\n <\/h4>\n <\/div>\n
      \n
      \n \n

      Exporters<\/h4>\n

      \n Risk-Rewards – Strategy beats Passives\n <\/h4>\n \"\"\n\n \"\"\n\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Risk<\/h2>\n

      MANAGEMENT<\/h4>\n Hedger gets Strategies<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n What are (1) comparative NAVS Passive vs\n Strategies(2)Odds(Hi-Lo-Avg) of Annual Returns (3) Hedge\n Costs – and Alpha.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Better Safe than Sorry<\/h5>\n Philosophy is ageless. Best practices are-not.<\/small\n >\n <\/div>\n\n

      \n Up-tier Hedging best practices with Strategies to drive your\n hedge\/de-hedge decisions and do far better than traditional\n hedges.\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      STR<\/td>\n 260<\/td>\n 3.3<\/td>\n -4<\/td>\n 13<\/td>\n <\/tr>\n
      ALL<\/td>\n 145<\/td>\n 1.3<\/td>\n -20<\/td>\n 16<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      Exporters<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      Exporters<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Always Long vs No Hedge.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 217<\/td>\n 2.59<\/td>\n -11<\/td>\n 15<\/td>\n <\/tr>\n
      Passive<\/td>\n 135<\/td>\n 0.99<\/td>\n -20<\/td>\n 16<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n USDINR Longs 1992-2023<\/span\n >\n A Learning(229) beats Passive (135)<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n
      \n
      \n
      \n
      \n

      ASIA<\/span>-10Y<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n USD Terms- NAV & Returns % p.a.<\/b>\n <\/div>\n\n
      \n
      \n \n

      ASIA-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n
      A LEARN<\/td>\n 454<\/td>\n 14<\/td>\n 7.22%<\/td>\n <\/tr>\n
      KOR<\/td>\n 284<\/td>\n 22<\/td>\n 4.93%<\/td>\n <\/tr>\n
      IND<\/td>\n 241<\/td>\n 26<\/td>\n 4.12%<\/td>\n <\/tr>\n
      MAL<\/td>\n 225<\/td>\n 12<\/td>\n 3.81%<\/td>\n <\/tr>\n
      AVG<\/td>\n 250<\/td>\n 20<\/td>\n 4.29%<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 454
      a Learn<\/span\n >\n <\/div>\n
      \n KOR<\/span>\n <\/div>\n
      \n IND<\/span>\n <\/div>\n
      \n MAL<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Asia 10Y Govt bond Returns in USD terms.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 454<\/td>\n 7.22<\/td>\n -14<\/td>\n 24<\/td>\n <\/tr>\n
      KOR10Y<\/td>\n 284<\/td>\n 4.93<\/td>\n -14<\/td>\n 28<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n Asia Gov 10Y Jan02-Sep23 $ Returns<\/span\n >\n KOR Best 284 MAL 225 IND 241. A Learning made 454<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n <\/div>\n <\/div>\n \n \n
      \n
      \n
      \n
      \n

      Equities<\/h2>\n

      \n Risk-Rewards –\n Strategy<\/span> beats\n Market<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      Equities<\/h4>\n

      Risk-Rewards – Strategy<\/span> beats Market<\/h4>\n \"\"\n\n \"\"\n\n\n \n
      \n Market<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Risk<\/h2>\n

      MANAGEMENT<\/h4>\n Trader gets Strategies<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n What are (1) comparative NAVS Market vs\n Strategies(2)Odds(5-95) of Annual Returns (3) Factory Tops\n of 100 K for last Full History, 3 Years, 1 Year\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Generate Better Odds<\/h5>\n <\/div>\n\n

      \n Get Classified Factory-Tops of 100K+ Strategies Selection\n ready for machine-lenses.Strategy pendulum has better odds\n than Market<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n DD<\/th>\n G\/L<\/th>\n Ret<\/th>\n <\/tr>\n
      1702<\/td>\n -17<\/td>\n 2.0<\/td>\n 12.5<\/td>\n <\/tr>\n
      672<\/td>\n -60<\/td>\n 1.1<\/td>\n 7.4<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n NIF FUT Jan00-Apr21<\/span\n >\n Strategy 1702 betters Market 672<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      Equities<\/h2>\n

      \n Alpha betters Your\n Strategy<\/span> Risk-Rewards<\/span\n >\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      Equities<\/h4>\n

      Alpha betters Your Strategy Risk- Rewards<\/h4>\n \"\"\n\n \"\"\n\n\n
      \n α – You<\/span>\n
      \n
      <\/div>\n
      <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Money<\/h2>\n

      MANAGEMENT<\/h4>\n Strategy gets Alpha<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n WITHOUT<\/span> changing my strategy\n can alpha improve its risk reward\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Catapult Your risk-reward<\/h5>\n <\/div>\n\n

      \n I trade My-Favorite-Strategy. Can-AI improve the\n risk-rewards of my strategy without changing it?\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n Loss<\/th>\n DD<\/th>\n <\/tr>\n
      33x<\/td>\n 16K<\/td>\n 14<\/td>\n <\/tr>\n
      25x<\/td>\n 27K<\/td>\n 28<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n NIF FUT Jan00-Apr21<\/span\n >\n Alpha-losses better than Strategy<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      Equities<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      Equities<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n
      \n \n
      \n Better the Best<\/span\n >\n 57<\/span\n >\n <\/div>\n <\/div>\n \n \n
      \n RIL<\/span>\n 27<\/span>\n <\/div>\n <\/div>\n \n
      \n SBI<\/span>\n 9<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n
      \n 57x<\/span>\n <\/div>\n
      \n RIL<\/span>\n <\/div>\n
      \n SBI<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in Capital allocation with\n lower risk between 2 stocks\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n I trade My-Favorite-Strategy. Can-AI improve the\n risk-rewards of my strategy without changing it?\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n G\/L<\/th>\n DD<\/th>\n <\/tr>\n
      57x<\/td>\n 1.1<\/td>\n -47<\/td>\n <\/tr>\n
      27x<\/td>\n 1.1<\/td>\n -68<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n Stocks 2003-20<\/span>\n earning 57x betters Best(27x,9x)<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      GOI<\/span> 10Y<\/h2>\n

      \n Risk-Rewards –\n Strategy<\/span> beats\n Market<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      GOI 10Y<\/h4>\n

      Risk-Rewards – Strategy beats Market<\/h4>\n \"\"\n\n \"\"\n\n\n \n
      \n Market<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Risk<\/h2>\n

      MANAGEMENT<\/h4>\n Trader gets Strategies<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n What are (1) comparative NAVS Market vs\n Strategies(2)Odds(5-95) of Annual Returns (3) Factory Tops\n of 100 K for last Full History, 3 Years, 1 Year\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Generate Better Odds<\/h5>\n <\/div>\n\n

      \n Get Classified Factory-Tops of 100K+ Strategies Selection\n ready for machine-lenses. Strategy pendulum has better odds\n than Market<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n DD<\/th>\n G\/L<\/th>\n <\/tr>\n
      741<\/td>\n -2<\/td>\n 5.5<\/td>\n <\/tr>\n
      595<\/td>\n -14<\/td>\n 1.3<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n GOI10Y Jan00-Apr21<\/span>\n Longs made 595\/14 Drawdown. Strategy 741\/2 drawdown<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      GOI<\/span>-10Y<\/h2>\n

      \n Alpha betters Your\n Strategy<\/span> Risk-Rewards<\/span\n >\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      GOI-10Y<\/h4>\n

      Alpha betters Your Strategy Risk-Rewards<\/h4>\n \"\"\n\n \"\"\n\n\n
      \n α – You<\/span>\n
      \n
      <\/div>\n
      <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Money<\/h2>\n

      MANAGEMENT<\/h4>\n Strategy gets Alpha<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n WITHOUT<\/span> changing my strategy\n can alpha improve its risk reward\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Catapult Your risk-reward<\/h5>\n <\/div>\n\n

      \n I trade My-Favorite-Strategy. Can-AI improve the\n risk-rewards of my strategy without changing it?\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      P&L<\/th>\n Loss<\/th>\n G\/L<\/th>\n <\/tr>\n
      1233<\/td>\n 412<\/td>\n 4.0<\/td>\n <\/tr>\n
      1412<\/td>\n 1756<\/td>\n 1.8<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n NIF FUT Jan00-Apr21<\/span\n >\n Alpha-losses better than Strategy<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      GOI<\/span>-10Y<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      GOI-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n
      \n \n
      \n LEARN<\/span\n >\n 956<\/span\n >\n <\/div>\n <\/div>\n \n \n
      \n GOI 10Y<\/span>\n 595<\/span>\n <\/div>\n <\/div>\n \n
      \n CALL<\/span>\n 441<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n
      \n 956\/07<\/span><\/span\n >\n <\/div>\n
      \n 664\/14<\/span><\/span\n >\n <\/div>\n
      \n 441\/00<\/span><\/span\n >\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Call & Bond portfolio.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning Total >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 956<\/td>\n 9.35<\/td>\n -2<\/td>\n 32<\/td>\n <\/tr>\n
      GOI10Y<\/td>\n 664<\/td>\n 7.85<\/td>\n -12<\/td>\n 34<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n GOI10Y-Call Jan00-Feb 24<\/span\n >\n Bond return 664,Call Money 441. Learning made 956 with\n Half the Risk.<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      CRUDE<\/span>-OIL<\/h2>\n <\/div>\n\n
      \n
      \n \n\n

      CRUDE-OIL<\/h4>\n

      <\/h4>\n \"\"\n\n \"\"\n\n\n
      \n 57x<\/span>\n <\/div>\n\n \n
      \n Market<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n \n

      Risk Money Capital<\/h4>\n Get Strategy Alpha Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Strategy beats markets, Alpha beats Strategy, Learning beats\n all.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Get Factory Tops of 100K+ with better Odds Selection ready.\n A sample strategy\n 1369\/-50<\/span> betters market\n odds 216 \/-100<\/span>. Alpha spins\n 3734\/-39<\/span> of the Strategy\n and Learning spins from these 3 above\n 19750 \/-60<\/span> Total >\n Sum of Parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n \n \n
      NAV<\/th>\n DD<\/th>\n G\/L<\/th>\n <\/tr>\n
      19750<\/td>\n -60<\/td>\n 1.2<\/td>\n <\/tr>\n
      3734<\/td>\n -29<\/td>\n 2.3<\/td>\n <\/tr>\n
      1369<\/td>\n -50<\/td>\n 1.7<\/td>\n <\/tr>\n
      216<\/td>\n < - 100<\/td>\n 1.0<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n CRUDE 1985-2022<\/span>\n Learning 197X vs Market 2 x<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      Equities<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n

      ASIA-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n \n\n \n \n \n \n \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n DD<\/th>\n <\/tr>\n
      A LEARN<\/td>\n 37<\/td>\n 53<\/td>\n <\/tr>\n
      FMCG<\/td>\n 18<\/td>\n 36<\/td>\n <\/tr>\n
      AUTO<\/td>\n 17<\/td>\n 62<\/td>\n <\/tr>\n
      BANK<\/td>\n 16<\/td>\n 69<\/td>\n <\/tr>\n
      IT<\/td>\n 13<\/td>\n 66<\/td>\n <\/tr>\n
      PHARMA<\/td>\n 8<\/td>\n 54<\/td>\n <\/tr>\n
      AVG<\/td>\n 14<\/td>\n 57<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 57x<\/span>\n <\/div>\n
      \n 18x<\/span>\n <\/div>\n
      \n 8x<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Equity Sectors like Auto FMCG Pharma etc.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      NAV<\/th>\n G\/L<\/th>\n DD<\/th>\n <\/tr>\n
      37x<\/td>\n 1.18<\/td>\n -53<\/td>\n <\/tr>\n
      18x<\/td>\n 1.15<\/td>\n -36<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n Stocks 2003-20<\/span>\n earning 57x betters Best(27x,9x)<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      Equities<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n <\/div>\n\n
      \n
      \n \n\n

      Equities<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n DD<\/th>\n <\/tr>\n
      A LEARN<\/td>\n 225<\/td>\n 23<\/td>\n <\/tr>\n
      EUR L<\/td>\n 68<\/td>\n 48<\/td>\n <\/tr>\n
      EUR S<\/td>\n 133<\/td>\n 30<\/td>\n <\/tr>\n
      GBP L<\/td>\n 73<\/td>\n 47<\/td>\n <\/tr>\n
      GBP S<\/td>\n 121<\/td>\n 27<\/td>\n <\/tr>\n
      USD L<\/td>\n 87<\/td>\n 31<\/td>\n <\/tr>\n
      USD S<\/td>\n 108<\/td>\n 26<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 225<\/span>\n <\/div>\n
      \n 133<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between G3 INR Long\/Short portfolio\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 225<\/td>\n 6.0<\/td>\n -15<\/td>\n 34<\/td>\n <\/tr>\n
      EUR\/S<\/td>\n 113<\/td>\n 2.1<\/td>\n -18<\/td>\n 25<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n G3-INR Jan10-Nov23<\/span>\n 6 Pairs Best Return EURINR Shorts 133 A Learning made\n 225<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      ASIA<\/span>-10Y<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n USD Terms- NAV & Returns % p.a.<\/b>\n <\/div>\n\n
      \n
      \n \n

      ASIA-10Y<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n
      A LEARN<\/td>\n 454<\/td>\n 14<\/td>\n 7.22%<\/td>\n <\/tr>\n
      KOR<\/td>\n 284<\/td>\n 22<\/td>\n 4.93%<\/td>\n <\/tr>\n
      IND<\/td>\n 241<\/td>\n 26<\/td>\n 4.12%<\/td>\n <\/tr>\n
      MAL<\/td>\n 225<\/td>\n 12<\/td>\n 3.81%<\/td>\n <\/tr>\n
      AVG<\/td>\n 250<\/td>\n 20<\/td>\n 4.29%<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 454
      a Learn<\/span\n >\n <\/div>\n
      \n KOR<\/span>\n <\/div>\n
      \n IND<\/span>\n <\/div>\n
      \n MAL<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n Can learning better the best in capital allocation with\n lower risk between Asia 10Y Govt bond Returns in USD terms.\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 454<\/td>\n 7.22<\/td>\n -14<\/td>\n 24<\/td>\n <\/tr>\n
      KOR10Y<\/td>\n 284<\/td>\n 4.93<\/td>\n -14<\/td>\n 28<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n Asia Gov 10Y Jan02-Sep23 $ Returns<\/span\n >\n KOR Best 284 MAL 225 IND 241. A Learning made 454<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n
      \n
      \n

      US<\/span>-Indices<\/h2>\n

      \n Better your\n Best<\/span>\n <\/h4>\n SNP500-UST10Y-FED<\/b>\n <\/div>\n\n
      \n
      \n \n\n

      US-Indices<\/h4>\n

      Better your Best<\/h4>\n \"\"\n\n \"\"\n\n\n \n \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n DD<\/th>\n <\/tr>\n
      A LEARN<\/td>\n 825<\/td>\n 34<\/td>\n <\/tr>\n
      SNP<\/td>\n 326<\/td>\n 57<\/td>\n <\/tr>\n
      UST10Y<\/td>\n 240<\/td>\n 25<\/td>\n <\/tr>\n
      FEDF<\/td>\n 154<\/td>\n 0<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n\n
      \n 825<\/span>\n <\/div>\n
      \n SNP<\/span>\n <\/div>\n
      \n UST10Y<\/span>\n <\/div>\n
      \n FED<\/span>\n <\/div>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n
      \n \"\"\n\n <\/div>\n <\/div>\n <\/div>\n
      \n
      \n

      Capital<\/h2>\n

      ALLOCATION<\/h4>\n Portfolio gets Learning<\/b>\n <\/div>\n\n
      \n
      Decision – Problem<\/h5>\n
      <\/div>\n <\/div>\n\n

      \n \n Can Learning better the best of SNP500 UST10Y Fed Funds in Capital allocation ?\n <\/p>\n\n

      \n
      Decision – Support<\/h5>\n
      <\/div>\n <\/div>\n\n
      \n \"\"\n
      Learn-to-Learn<\/h5>\n <\/div>\n\n

      \n Better your-best. Get Selection & Capital Allocation\n decisions with many ways of spinning\n Total<\/span> >\n Sum of the parts<\/span>\n <\/p>\n\n

      Risk-Reward<\/h5>\n\n \n
      \n \n \n \n \n \n
      Risk<\/th>\n NAV<\/th>\n Ret<\/th>\n Low<\/th>\n High<\/th>\n <\/tr>\n
      LEARN<\/td>\n 825<\/td>\n 9.2<\/td>\n -9<\/td>\n 22<\/td>\n <\/tr>\n
      SNP500<\/td>\n 326<\/td>\n 5.0<\/td>\n -60<\/td>\n 40<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n
      \n \n arrow_upward\n <\/span>\n <\/div>\n <\/div>\n\n
      \n US TOP Indices 2000-NOV 23<\/span\n >\n A Learning(8.2x) beats the best (3.2x)<\/span\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n\n \n
      \n
      \n \n \n <\/circle>\n <\/circle>\n <\/path>\n <\/svg>\n <\/div>\n
      Corporates<\/h5>\n <\/div>\n
      \n
      \n
      \n
      \n
      <\/div>\n <\/div>\n
      \n
      \n

      Business Priority<\/p>\n <\/div>\n

      \n Your core business is-not markets. Your Core Operating\n surplus or deficits place unique demands to manage\n Investments better for higher risk-rewards and to manage\n borrowings better to reduce funding costs- especially\n Foregin Currency borrowings with hedges or Importer with\n same hedge demand.\n <\/p>\n\n

      \n

      Opportunity Costs<\/p>\n <\/div>\n\n

      \n Investment Policies<\/strong> have ever evolving\n risk-reward choices even within your constraints on risk\n universe for e.g. (a)Credit constraint like only Government\n of India Securities ( G-Sec ); (b)Tenor constraint like 1-10\n Y;(c) Liquidity constraint like most actively traded ; and\n so on .Are you continuosly aligning your decisions to better\n your risk-rewards. Even without being a Trader you can make\n a few churns per annum on investments and transform the\n risk-reward payoffs.\n <\/p>\n\n

      \n Hedge Policies<\/strong> Hedge what – All moves or\n Adverse moves?\n <\/p>\n\n

      \n Traditionally hedging is seen as a cost centre or\n responsibility centre activity to hedge some-amount, for\n some-tenor blessed by some-policy. Typically a charitable\n 20%- was left discretionary 80%+ being non-discretionary.\n Systematic hedges can add significant alpha by\n hedge\/de-hedge decisions pursuing twin objectives- hedge\n when moves are against and de-hedge when moves are in favour\n doing balancing actions of risk-reward with cost-benefit\n calculations;real-time & every-time.\n <\/p>\n <\/div>\n <\/div>\n

      \n
      \n
      \n

      Man – Machine Mix<\/p>\n <\/div>\n

      \n Traditional organizations were Man-Heavy style for Treasury\n actions; fundamental research; Investment and Hedge\n policies- which were mere philosophies and faith; without\n much room for best practices;more so as core business is\n different.\n <\/p>\n

      \n AI<\/span> can enhance your traditonal\n wisdom with ; field work that is far more detailed, far more\n current, far more fit for repetitive actions and continuous\n evaluations; far more granular digital-footprints of\n What-Why-How-When-Who made those decision-choices thereby\n making it feasible<\/strong> to frame better\n policy-choices and practices for a more live ,agile and\n continuous alignment with evolving opportunities within your\n risk-universe.\n <\/p>\n\n

      \n

      Opportunity Scoreboard<\/p>\n <\/div>\n

      \n Zero<\/span> Human Mode<\/strong\n >
      \n As per your settings Machine can perform Opportunity hunt\n and Signal-trades in Opportunity-Blotter across Trading\n Hedging Investment\/Portfolio domains. Our unique AI\n Domain-Driver(learning-process) and Selection wizard make\n this feasible.\n <\/p>\n\n

      \n Man-<\/span>Machine Mode<\/strong\n >
      \n Better the benchmark opportunity costs with futher\n custom-settings of your team in man machine mode. Multiply\n team value-add with Traders Multiplier.\n <\/p>\n\n

      \n The firm gets benefit of machine driven opportunity hunt &\n opportunity costs discovery on a continuous basis across\n markets; even with ZERO headcount.\n <\/p>\n <\/div>\n

      \n
      <\/div>\n <\/div>\n <\/div>\n\n \n \n \"aialgos\n <\/a>\n
      \n copyrights – 2010-24 | aialgos<\/span\n >\u00ae<\/sup>\n <\/div>\n
      \n \"\"\n\n Use-Case<\/a\n >\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n \n \n
      \n
      \n
      \n \n ×<\/span>\n <\/div>\n
      Image Preview<\/h5>\n

      Image Preview sub<\/h3>\n \"Selected\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n
      \n
      1<\/div>\n
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      13<\/div>\n <\/div>\n","protected":false},"excerpt":{"rendered":"

      Use Cases Home Use Cases Traders Multiplier Work-out HISTORY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Start with 0.25 million back tests on your Risk,Money,Capital decisions; add more. Generate Better Odds Catapult Your risk reward Better Your Best Banks Use Cases 4-9 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-322","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/pages\/322","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/comments?post=322"}],"version-history":[{"count":33,"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/pages\/322\/revisions"}],"predecessor-version":[{"id":527,"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/pages\/322\/revisions\/527"}],"wp:attachment":[{"href":"https:\/\/uat.crayoninfotech.com\/ai-algos\/wp-json\/wp\/v2\/media?parent=322"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}