More on Economics & Investing
1 year ago
Donor-Advised Fund Tax Benefits (DAF)
Giving through a donor-advised fund can be tax-efficient. Using a donor-advised fund can reduce your tax liability while increasing your charitable impact.
Grow Your Donations Tax-Free.
Your DAF's charitable dollars can be invested before being distributed. Your DAF balance can grow with the market. This increases grantmaking funds. The assets of the DAF belong to the charitable sponsor, so you will not be taxed on any growth.
Avoid a Windfall Tax Year.
DAFs can help reduce tax burdens after a windfall like an inheritance, business sale, or strong market returns. Contributions to your DAF are immediately tax deductible, lowering your taxable income. With DAFs, you can effectively pre-fund years of giving with assets from a single high-income event.
Make a contribution to reduce or eliminate capital gains.
One of the most common ways to fund a DAF is by gifting publicly traded securities. Securities held for more than a year can be donated at fair market value and are not subject to capital gains tax. If a donor liquidates assets and then donates the proceeds to their DAF, capital gains tax reduces the amount available for philanthropy. Gifts of appreciated securities, mutual funds, real estate, and other assets are immediately tax deductible up to 30% of Adjusted gross income (AGI), with a five-year carry-forward for gifts that exceed AGI limits.
Using Appreciated Stock as a Gift
Donating appreciated stock directly to a DAF rather than liquidating it and donating the proceeds reduces philanthropists' tax liability by eliminating capital gains tax and lowering marginal income tax.
In the example below, a donor has $100,000 in long-term appreciated stock with a cost basis of $10,000:
Using a DAF would allow this donor to give more to charity while paying less taxes. This strategy often allows donors to give more than 20% more to their favorite causes.
For illustration purposes, this hypothetical example assumes a 35% income tax rate. All realized gains are subject to the federal long-term capital gains tax of 20% and the 3.8% Medicare surtax. No other state taxes are considered.
The information provided here is general and educational in nature. It is not intended to be, nor should it be construed as, legal or tax advice. NPT does not provide legal or tax advice. Furthermore, the content provided here is related to taxation at the federal level only. NPT strongly encourages you to consult with your tax advisor or attorney before making charitable contributions.
Sofien Kaabar, CFA
1 year ago
How to Make a Trading Heatmap
Python Heatmap Technical Indicator
Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.
The Market System
Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.
Sideways: The market tends to fluctuate while staying within predetermined zones.
Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.
Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.
If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.
Indicator of Relative Strength
J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:
Determine the difference between the closing prices from the prior ones.
Distinguish between the positive and negative net changes.
Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.
Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.
To obtain the RSI, use the normalization formula shown below for each time step.
The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.
import numpy as np def add_column(data, times): for i in range(1, times + 1): new = np.zeros((len(data), 1), dtype = float) data = np.append(data, new, axis = 1) return data def delete_column(data, index, times): for i in range(1, times + 1): data = np.delete(data, index, axis = 1) return data def delete_row(data, number): data = data[number:, ] return data def ma(data, lookback, close, position): data = add_column(data, 1) for i in range(len(data)): try: data[i, position] = (data[i - lookback + 1:i + 1, close].mean()) except IndexError: pass data = delete_row(data, lookback) return data def smoothed_ma(data, alpha, lookback, close, position): lookback = (2 * lookback) - 1 alpha = alpha / (lookback + 1.0) beta = 1 - alpha data = ma(data, lookback, close, position) data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta) for i in range(lookback + 2, len(data)): try: data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta) except IndexError: pass return data def rsi(data, lookback, close, position): data = add_column(data, 5) for i in range(len(data)): data[i, position] = data[i, close] - data[i - 1, close] for i in range(len(data)): if data[i, position] > 0: data[i, position + 1] = data[i, position] elif data[i, position] < 0: data[i, position + 2] = abs(data[i, position]) data = smoothed_ma(data, 2, lookback, position + 1, position + 3) data = smoothed_ma(data, 2, lookback, position + 2, position + 4) data[:, position + 5] = data[:, position + 3] / data[:, position + 4] data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5]))) data = delete_column(data, position, 6) data = delete_row(data, lookback) return data
Make sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.
My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:
Using the Heatmap to Find the Trend
RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:
When the RSI is higher than 50, a green vertical line is drawn.
When the RSI is lower than 50, a red vertical line is drawn.
Zooming out yields a basic heatmap, as shown below.
def indicator_plot(data, second_panel, window = 250): fig, ax = plt.subplots(2, figsize = (10, 5)) sample = data[-window:, ] for i in range(len(sample)): ax.vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1) if sample[i, 3] > sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5) if sample[i, 3] < sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) if sample[i, 3] == sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) ax.grid() for i in range(len(sample)): if sample[i, second_panel] > 50: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5) if sample[i, second_panel] < 50: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5) ax.grid() indicator_plot(my_data, 4, window = 500)
Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.
Another suggestion is to develop an RSI Heatmap for Extreme Conditions.
Contrarian indicator RSI. The following rules apply:
Whenever the RSI is approaching the upper values, the color approaches red.
The color tends toward green whenever the RSI is getting close to the lower values.
Zooming out yields a basic heatmap, as shown below.
import matplotlib.pyplot as plt def indicator_plot(data, second_panel, window = 250): fig, ax = plt.subplots(2, figsize = (10, 5)) sample = data[-window:, ] for i in range(len(sample)): ax.vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1) if sample[i, 3] > sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5) if sample[i, 3] < sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) if sample[i, 3] == sample[i, 0]: ax.vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5) ax.grid() for i in range(len(sample)): if sample[i, second_panel] > 90: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5) if sample[i, second_panel] > 80 and sample[i, second_panel] < 90: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5) if sample[i, second_panel] > 70 and sample[i, second_panel] < 80: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5) if sample[i, second_panel] > 60 and sample[i, second_panel] < 70: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5) if sample[i, second_panel] > 50 and sample[i, second_panel] < 60: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5) if sample[i, second_panel] > 40 and sample[i, second_panel] < 50: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5) if sample[i, second_panel] > 30 and sample[i, second_panel] < 40: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5) if sample[i, second_panel] > 20 and sample[i, second_panel] < 30: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5) if sample[i, second_panel] > 10 and sample[i, second_panel] < 20: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5) if sample[i, second_panel] > 0 and sample[i, second_panel] < 10: ax.vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5) ax.grid() indicator_plot(my_data, 4, window = 500)
Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation.
Technical analysis will lose its reputation as subjective and unscientific.
When you find a trading strategy or technique, follow these steps:
Put emotions aside and adopt a critical mindset.
Test it in the past under conditions and simulations taken from real life.
Try optimizing it and performing a forward test if you find any potential.
Transaction costs and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be considered in your tests.
After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.
1 year ago
I created BAW-IV Trading because I was short on money.
More retail traders means faster, more sophisticated, and more successful methods.
Only requires a laptop and an internet connection.
We'll use OpenBB's research platform for data/analysis.
Pricing and execution on Options-Quant
You don't need to know the arithmetic details to use this method.
Black-Scholes is a popular option pricing model. It's best for pricing European options. European options are only exercisable at expiration, unlike American options. American options are always exercisable.
American options carry a premium to cover for the risk of early exercise. The Black-Scholes model doesn't account for this premium, hence it can't price genuine, traded American options.
Barone-Adesi-Whaley (BAW) model. BAW modifies Black-Scholes. It accounts for exercise risk premium and stock dividends. It adds the option's early exercise value to the Black-Scholes value.
The trader need not know the formulaic derivations of this model.
This strategy targets implied volatility. First, we'll locate liquid options that expire within 30 days and have minimal implied volatility.
After selecting the option that meets the requirements, we price it to get the BAW implied volatility (we choose BAW because it's a more accurate Black-Scholes model). If estimated implied volatility is larger than market volatility, we'll capture the spread.
(Calculated IV — Market IV) = (Profit)
Some approaches to target implied volatility are pricey and inaccessible to individual investors. The best and most cost-effective alternative is to acquire a straddle and delta hedge. This may sound terrifying and pricey, but as shown below, it's much less so.
First, we want to find our ideal option, so we use OpenBB terminal to screen for options that:
Have an IV at least 5% lower than the 20-day historical IV
Are no more than 5% out-of-the-money
Expire in less than 30 days
stocks/options/screen/set low_IV/scr --export Output.csv
This uses the screener function to screen for options that satisfy the above criteria, which we specify in the low IV preset (more on custom presets here). It then saves the matching results to a csv(Excel) file for viewing and analysis.
Stick to liquid names like SPY, AAPL, and QQQ since getting out of a position is just as crucial as getting in. Smaller, illiquid names have higher inefficiencies, which could restrict total profits.
We calculate IV using the BAWbisection model (the bisection is a method of calculating IV, more can be found here.) We price the IV first.
According to the BAW model, implied volatility at this level should be priced at 26.90%. When re-pricing the put, IV is 24.34%, up 3%.
Now it's evident. We must purchase the straddle (long the call and long the put) assuming the computed implied volatility is more appropriate and efficient than the market's. We just want to speculate on volatility, not price fluctuations, thus we delta hedge.
The Fun Starts
We buy both options for $7.65. (x100 multiplier). Initial delta is 2. For every dollar the stock price swings up or down, our position value moves $2.
We want delta to be 0 to avoid price vulnerability. A delta of 0 suggests our position's value won't change from underlying price changes. Being delta-hedged allows us to profit/lose from implied volatility. Shorting 2 shares makes us delta-neutral.
That's delta hedging. (Share price * shares traded) = $330.7 to become delta-neutral. You may have noted that delta is not truly 0.00. This is common since delta-hedging means getting as near to 0 as feasible, since it is rare for deltas to align at 0.00.
Now we're vulnerable to changes in Vega (and Gamma, but given we're dynamically hedging, it's not a big risk), or implied volatility. We wanted to gamble that the position's IV would climb by at least 2%, so we'll maintain it delta-hedged and watch IV.
Because the underlying moves continually, the option's delta moves continuously. A trader can short/long 5 AAPL shares at most. Paper trading lets you practice delta-hedging. Being quick-footed will help with this tactic.
As expected, implied volatility rose. By 10 minutes before market closure, the call's implied vol rose to 27% and the put's to 24%. This allowed us to sell the call for $4.95 and the put for $4.35, creating a profit of $165.
You may pull historical data to see how this trade performed. Note the implied volatility and pricing in the final options chain for August 5, 2022 (the position date).
Congratulations, that was a doozy. To reiterate, we identified tickers prone to increased implied volatility by screening OpenBB's low IV setting. We double-checked the IV by plugging the price into Options-BAW Quant's model. When volatility was off, we bought a straddle and delta-hedged it. Finally, implied volatility returned to a normal level, and we profited on the spread.
The retail trading space is very quickly catching up to that of institutions. Commissions and fees used to kill this method, but now they cost less than $5. Watching momentum, technical analysis, and now quantitative strategies evolve is intriguing.
I'm not linked with these sites and receive no financial benefit from my writing.
Tell me how your experience goes and how I helped; I love success tales.
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8 months ago
Understanding several Value Proposition kinds will help you create better goods.
Fixing problems isn't enough.
Numerous articles and how-to guides on value propositions focus on fixing consumer concerns.
Contrary to popular opinion, addressing customer pain rarely suffices. Win your market category too.
Core Value Statement
Value proposition usually means a product's main value.
Its how your product solves client problems. The product's core.
Answering these questions creates a relevant core value proposition:
What tasks is your customer trying to complete? (Jobs for clients)
How much discomfort do they feel while they perform this? (pains)
What would they like to see improved or changed? (gains)
After that, you create products and services that alleviate those pains and give value to clients.
Value Proposition by Category
Your product belongs to a market category and must follow its regulations, regardless of its value proposition.
Creating a new market category is challenging. Fitting into customers' product perceptions is usually better than trying to change them.
New product users simplify market categories. Products are labeled.
Your product will likely be associated with a collection of products people already use.
Example: IT experts will use your communication and management app.
If your target clients think it's an advanced mail software, they'll compare it to others and expect things like:
adequate storage space
list of contacts
If your target users view your product as a task management app, things change. You can survive without a contact list, but not status management.
Find out what your customers compare your product to and if it fits your value offer. If so, adapt your product plan to dominate this market. If not, try different value propositions and messaging to put the product in the right context.
Finished Value Proposition
A comprehensive value proposition is when your solution addresses user problems and wins its market category.
Addressing simply the primary value proposition may produce a valuable and original product, but it may struggle to cross the chasm into the mainstream market. Meeting expectations is easier than changing views.
Without a unique value proposition, you will drown in the red sea of competition.
Find out who your target consumer is and what their demands and problems are.
To meet these needs, develop and test a primary value proposition.
Speak with your most devoted customers. Recognize the alternatives they use to compare you against and the market segment they place you in.
Recognize the requirements and expectations of the market category.
To meet or surpass category standards, modify your goods.
Great products solve client problems and win their category.
11 months ago
Why NFTs Have a Bright Future Away from Collectible Art After Punks and Apes
After a crazy second half of 2021 and significant trade volumes into 2022, the market for NFT artworks like Bored Ape Yacht Club, CryptoPunks, and Pudgy Penguins has begun a sharp collapse as market downturns hit token values.
DappRadar data shows NFT monthly sales have fallen below $1 billion since June 2021. OpenSea, the world's largest NFT exchange, has seen sales volume decline 75% since May and is trading like July 2021.
Prices of popular non-fungible tokens have also decreased. Bored Ape Yacht Club (BAYC) has witnessed volume and sales drop 63% and 15%, respectively, in the past month.
BeInCrypto analysis shows market decline. May 2022 cryptocurrency marketplace volume was $4 billion, according to a news platform. This is a sharp drop from April's $7.18 billion.
OpenSea, a big marketplace, contributed $2.6 billion, while LooksRare, Magic Eden, and Solanart also contributed.
NFT markets are digital platforms for buying and selling tokens, similar stock trading platforms. Although some of the world's largest exchanges offer NFT wallets, most users store their NFTs on their favorite marketplaces.
In January 2022, overall NFT sales volume was $16.57 billion, with LooksRare contributing $11.1 billion. May 2022's volume was $12.57 less than January, a 75% drop, and June's is expected to be considerably smaller.
A World Based on Utility
Despite declines in NFT trading volumes, not all investors are negative on NFTs. Although there are uncertainties about the sustainability of NFT-based art collections, there are fewer reservations about utility-based tokens and their significance in technology's future.
In June, business CEO Christof Straub said NFTs may help artists monetize unreleased content, resuscitate catalogs, establish deeper fan connections, and make processes more efficient through technology.
We all know NFTs can't be JPEGs. Straub noted that NFT music rights can offer more equitable rewards to musicians.
Music NFTs are here to stay if they have real value, solve real problems, are trusted and lawful, and have fair and sustainable business models.
NFTs can transform numerous industries, including music. Market opinion is shifting towards tokens with more utility than the social media artworks we're used to seeing.
While the major NFT names remain dominant in terms of volume, new utility-based initiatives are emerging as top 20 collections.
Otherdeed, Sorare, and NBA Top Shot are NFT-based games that rank above Bored Ape Yacht Club and Cryptopunks.
Users can switch video NFTs of basketball players in NBA Top Shot. Similar efforts are emerging in the non-fungible landscape.
Sorare shows how NFTs can support a new way of playing fantasy football, where participants buy and swap trading cards to create a 5-player team that wins rewards based on real-life performances.
Sorare raised 579.7 million in one of Europe's largest Series B financing deals in September 2021. Recently, the platform revealed plans to expand into Major League Baseball.
Strong growth indications suggest a promising future for NFTs. The value of art-based collections like BAYC and CryptoPunks may be questioned as markets become diluted by new limited collections, but the potential for NFTs to become intrinsically linked to tangible utility like online gaming, music and art, and even corporate reward schemes shows the industry has a bright future.
1 year ago
Yuga Labs’ Otherdeeds NFT mint triggers backlash from community
Unhappy community members accuse Yuga Labs of fraud, manipulation, and favoritism over Otherdeeds NFT mint.
Following the Otherdeeds NFT mint, disgruntled community members took to Twitter to criticize Yuga Labs' handling of the event.
Otherdeeds NFTs were a huge hit with the community, selling out almost instantly. Due to high demand, the launch increased Ethereum gas fees from 2.6 ETH to 5 ETH.
But the event displeased many people. Several users speculated that the mint was “planned to fail” so the group could advertise launching its own blockchain, as the team mentioned a chain migration in one tweet.
Others like Mark Beylin tweeted that he had "sold out" on all Ape-related NFT investments after Yuga Labs "revealed their true colors." Beylin also advised others to assume Yuga Labs' owners are “bad actors.”
Some users who failed to complete transactions claim they lost ETH. However, Yuga Labs promised to refund lost gas fees.
CryptoFinally, a Twitter user, claimed Yuga Labs gave BAYC members better land than non-members. Others who wanted to participate paid for shittier land, while BAYCS got the only worthwhile land.
The Otherdeed NFT drop also increased Ethereum's burn rate. Glassnode and Data Always reported nearly 70,000 ETH burned on mint day.