Finally deployed my 1st web-app on Streamlit, and it took quite some time, but i'm finally through😮‍💨. This is a loan classifier which predicts whether a customer is likely to accept or reject the loan offer(cont'd in thread. ).Do checkout👇🏻.@OpenLearn_NITJ.

Day-6 in the Finance League @OpenLearn_NITJ ✅️.A intro to the 'Commodities and Forex Markets' - participants, importance & various factors at play (varying from interest rates, geopolitical outlook, and demand-supply dynamics) which shape these markets. 📊Week well rounded up.

And we're just a month into this. 🔥. W Pathfinders 👏🏻.@OpenLearn_NITJ.

Day-5 in the Finance League @OpenLearn_NITJ.Where we learn 'Futures Pricing' & understand Futures v/s Options Hedging (with a fun analogy). I have one too:.Futures Hedging is for the committed-staying till the end, while Options Hedging is dating at will (without any obligations).

This was a completion✅️ of Day-21 and also Day-22 in the ML league @OpenLearn_NITJ.Kudos to pathfinders @VatsalKhanna55.@adexxhhh @Ratinder_999 @Kunal2417_ and Vivek sir for their super blogs. Hopefully this is the first of many more apps and now onto building another one 💪🏻📈.

I used Logistic Regresion (a Supervised classification technique) while we also take a look at the performance of RandomForest Calssifier, to compare the 2 models and the users can make their own prediction(s) as well in the page provided. And we also take a look at the dataset.

Through different input parameters, the model makes a prediction for (let's say) Banks/agencies etc. while also calculating the probability for accepting/rejecting the loan by the customer, & can help target specific customers, providing some business value, adding new customers.

And we're still not accounting the Greeks yet, especially Theta, Delta and Vega. alongwith Gamma & Rho. 🔹️Theta decay->long options' keep losing value while approaching expiry),.🔹️Vega & the IV factor,and.🔹️the price sensitivity of options due to change in price of asset.

And in this case, we bet the market (the asset actually) to take a steep fall, as we buy a 'put' in anticipation of 📉 while also receiving a premium, having written/sold a call option. BUT again, we lose big (& may face margin call) if the market 📈 against our position instead.

This is a strategic long call, where we make plenty as the spot price at expiry keeps rising📈 & the long call is ITM. As for the short 'put', we keep the premium received for having written the option, BUT the losses will be huge in case market moves in the opposite direction📉

As part of Day-4 @OpenLearn_NITJ, today I plotted visualizations of the payoffs for synthetic "Multi-leg" Options' strategies using python & here are 2 of 'em, both giving us an idea of the total payoff at different closing prices (i.e. spot at expiry). Checkout in the thread. 👇🏻.

And in this case, we write (sell) a put option at strike price 20000 while receiving a premium of Rs.200. Now for price at expiry>=20000,we make profit (max.) equal to premium but for a close < 20000, we loss heavily📉 with losses compounding (per lot) in case of overleveraging.

In this case, anticipating an index rally, we buy a call option (Long) at strike price 25000, paying a premium of Rs.100.If spot price at expiry is </= 25000, we lose only the premium paid, while making hefty returns 📈 (per lot) for price at expiry > 25000 (in this single-leg).

Day-4 in the Finance League📊 @OpenLearn_NITJ .A day where we take a deep dive into Options- Call & Put with both long/short position(s), alongwith Greeks and some python implementation for payoff visualizations.✅️.#ML.#Finance.Checkout here.👇🏻.

So a few completions of the past few days in the ML league @OpenLearn_NITJ.Day-19 ✅️ Guide to Feature Engineering (will share the implementation separately).and, Day-20✅️ Cross Validation, Grid Search CV & Randomized Search CV. (Posting this late, been into some prev. tasks).

Here's what i think. Daily P/L settlements help reduce credit/default risk involved for both the buyer & seller, with every profit/loss settling into the margin account(s), hence no overleveraging. Also, this helps creating liquidity if trader wishes to exit the contract early.

Day-3 in the Finance League📊 @OpenLearn_NITJ where we learn the basics of 'Financial Derivatives',.4 of 'em along with their types, usage, few e.g.'s and the risks involved, with an intriguing🧠QOD to wrap up the blog & the day. ✅️.Why futures require daily MTM settlements?.👇🏻.

And just when you complete the blog by @Ratinder_999 sir for Day-18 and learn 'Pipeline', this snippet from KMeans Clustering (Day-11) makes a lot more sense now.💡. Yeah, connecting the dots, as they say. and building a pipeline is really handy, the explanatory blog even more👏🏻

Completed Day-18 in the ML league @OpenLearn_NITJ.Learnt Pipeline along with ColumnTransformer which helps in setting up a smooth workflow while working with ML models. Also built a pipeline (with Logistic Regression model) as task for the day. Checkout👇🏻.

I'd look for angel investors or Venture Capital funding in early (pre-IPO) stages, rather than raising debt. Then in the growth and expansion stage, we make use of shares📊 (part of equity instruments) through an IPO to raise funds while giving ownership rights, & ESOPs later on.