Added detailed Sim object.

Added random trading.
Added halving logic for dividends.
Added modified illustrations of apy vs ubi charts.

econ-demo.py

@@ -1,18 +1,30 @@
 
 import pandas as pd
 import matplotlib.pyplot as plt
+import numpy as np
+import random
+import sys
+import tabulate
+
+# it = 0
 
 def compounding_interest(balances, rate=0.05, terms=3):
     if terms <= 0:
         print("Number of terms must be >0!")
         return
-    new_bal = []
+    global it 
-    for balance in balances:
+    balances_over_time = []
-        b = balance
+    current_balances = balances
-        for i in range(0,terms):
+    for i in range(0, terms):
-            b = b + (b * rate)
+        new_bal = []
-        new_bal.append(b)
+        for balance in current_balances:
-    return new_bal
+            b = balance + (balance * rate)
+            new_bal.append(b)
+            # it += 1
+        assert(len(new_bal) == len(balances))
+        balances_over_time.append(new_bal)
+        current_balances = new_bal
+    return balances_over_time
 
 def calc_share_of_wealth(balances):
     total_money = sum(balances)
@@ -21,32 +33,46 @@ def calc_share_of_wealth(balances):
         shares.append(balance/total_money)
     return shares
 
-def ubi(balances, dividend=10, terms=3):
+def ubi(balances, rate=10, terms=3):
-    new_bal = []
+    dividend = rate
-    for balance in balances:
+    balances_over_time = []
-        b = balance
+    current_balances = balances
-        for i in range(0,terms):
+    for i in range(0, terms):
-            b = b + dividend
+        new_bal = []
-        new_bal.append(b)
+        for balance in current_balances:
-    return new_bal
+            b = balance + dividend
+            new_bal.append(b)
+        assert(len(new_bal) == len(balances))
+        balances_over_time.append(new_bal)
+        current_balances = new_bal
+    return balances_over_time
 
-def get_balances_over_time(names, balances, func, terms=4):
+def mining(balances, halving_frequency=1, reward=10, terms=3):
-    if func is None:
+    balances_over_time = []
-        print("Need a function!")
+    current_balances = balances
-        return
+    for i in range(0, terms):
+        new_bal = []
+        for balance in current_balances:
+            b = balance + reward
+            new_bal.append(b)
+        assert(len(new_bal) == len(balances))
+        if i % halving_frequency == 0:
+            reward = reward/2
+        balances_over_time.append(new_bal)
+        current_balances = new_bal
+    return balances_over_time
+
+def get_balances_over_time(names, balances, data):
     if len(names) != len(balances):
         print("Every person needs a balance.")
         return
-    data = {
+    df = pd.DataFrame({
         'name' : names,
         'initial' : balances
-    }
+    })
-    df = pd.DataFrame(data)
+    for i in range(0, len(data)):
-    for t in range(1,terms):
+        key = str(i+1)
-        key = str(t)
+        df[key] = data[i]
-        frame = func(balances, terms=t)
-        if frame is not None:
-            df[key] = frame
     return df
 
 # -------------
@@ -64,8 +90,9 @@ def illustrate_share_of_wealth():
     print("\n")
 
     df = get_balances_over_time (
-        participants, balances, compounding_interest
+        participants, balances, compounding_interest(balances)
     )
+    # print(it)
     df["share"] = [val * 100 for val in calc_share_of_wealth(df["3"]) ]
     
     print(df.to_html())
@@ -74,22 +101,22 @@ def illustrate_share_of_wealth():
     print(f"How much money exists after {terms} terms?")
     #### In 222 terms, even C becomes a millionaire.
     nb = compounding_interest(balances, terms=terms)
-    print(nb)
+    print(nb[-1])
     print("\n")
 
     df = get_balances_over_time (
-        participants, balances, ubi
+        participants, balances, ubi(balances)
     )
     print(df.to_html())
 
     print("How much money exists after 999 terms?")
     u = ubi(balances, terms=999)
-    print(u)
+    print(u[-1])
 
     print("What is the share of wealth in a UBI economy?")
-    print(calc_share_of_wealth(u))
+    print(calc_share_of_wealth(u[-1]))
+
 
-illustrate_share_of_wealth()
 
 def visualize_ubi(terms=25):
 
@@ -97,8 +124,7 @@ def visualize_ubi(terms=25):
     balances = [100,40,20]
 
     df = get_balances_over_time (
-        participants, balances, ubi,
+        participants, balances, ubi(balances, terms=terms)
-        terms = terms
     )
     # print(df.keys)
     shares = []
@@ -109,8 +135,283 @@ def visualize_ubi(terms=25):
             continue
         shares.append(calc_share_of_wealth(data))
     sow = pd.DataFrame(shares)
+    x = [i for i in range(0,terms)]
+    try:
+        assert(len(x) == len(shares))
+    except AssertionError:
+        print(len(x), len(shares))
+        exit()
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, sow[0], color="red", label=participants[0]
+    )
+    plt.plot(
+        x, sow[1], color="lightgreen", label=participants[1]
+    )
+    plt.plot(
+        x, sow[2], color="cyan", label=participants[2]
+    )
+    plt.axhline(
+        y=0.33, color='violet', linestyle='--', label="0.33"
+    )
+
+    plt.title("Change in Wealth Distribution With a Constant Dividend")
+    plt.legend()
+    plt.xlabel("Terms")
+    plt.ylabel("Share of Wealth")
+    plt.savefig("ubi-wealth-distribution.png")
+    plt.close()
+    # plt.show()
+    return
+
+def calc_total_supply(df):
+    total = []
+    for key, data in df.items():
+        if key == "name":
+            continue
+        total.append(sum(data))
+    return total
+
+def draw_apy_inflation(terms=50, linear_label="? (linear)"):
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
+
+    df = get_balances_over_time (
+        participants, balances, ubi(balances, terms=terms)
+    )
+    total_supply_ubi = calc_total_supply(df)
+
+    df_si = get_balances_over_time (
+        participants, balances, 
+        compounding_interest(balances, terms=terms)
+    )
+    total_supply_si = calc_total_supply(df_si)
+    # + 1 because the initial frame is included this time
+    x = [i for i in range(0,terms+1)]
+    try:
+        assert(len(x) == len(total_supply_si))
+    except AssertionError:
+        print(len(x), len(total_supply_si))
+        print(df_si)
+        exit()
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, total_supply_ubi, color="cyan", label="dividend = 10"
+    )
+    plt.plot(
+        x, total_supply_si, color="red", label="apy = 0.05"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            compounding_interest(balances, terms=terms, rate=0.04)
+        )),
+        color="orange", label="apy = 0.04"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            compounding_interest(balances, terms=terms, rate=0.03)
+        )),
+        color="yellow", label="apy = 0.03"
+    )
+    plt.title("Supply of Money Over Time")
+    plt.legend()
+    plt.xlabel("Terms")
+    plt.ylabel("Total Currency")
+    plt.savefig("inflation-ubi-vs-5apy.png")
+    plt.close()
+    # plt.show()
+
+def draw_apy_inflation2(terms=50, linear_label="? (linear)"):
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
+
+    df = get_balances_over_time (
+        participants, balances, ubi(balances, terms=terms)
+    )
+    total_supply_ubi = calc_total_supply(df)
+
+    df_si = get_balances_over_time (
+        participants, balances, 
+        compounding_interest(balances, terms=terms)
+    )
+    total_supply_si = calc_total_supply(df_si)
+    # + 1 because the initial frame is included this time
+    x = [i for i in range(1913,1913+terms+1)]
+    try:
+        assert(len(x) == len(total_supply_si))
+    except AssertionError:
+        print("Assert Error:")
+        print(len(x), len(total_supply_si))
+        print(df_si)
+        exit()
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, total_supply_ubi, color="cyan", label="dividend = 10"
+    )
+    plt.plot(
+        x, total_supply_si, color="red", label="apy = 0.05"
+    )
+    plt.axvline(x=1960, color='yellow', linestyle='--')
+    plt.title("Supply of Money Over Time")
+    plt.legend()
+    plt.xlabel("Year")
+    plt.ylabel("Total Currency")
+    plt.savefig("inflation-ubi-vs-5apy2.png")
+    plt.close()
+    # plt.show()
+
+def draw_apy_inflation3(terms=50, linear_label="? (linear)"):
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
+
+    df = get_balances_over_time (
+        participants, balances, ubi(balances, terms=terms)
+    )
+    total_supply_ubi = calc_total_supply(df)
+
+    df_si = get_balances_over_time (
+        participants, balances, 
+        compounding_interest(balances, terms=terms)
+    )
+    total_supply_si = calc_total_supply(df_si)
+    # + 1 because the initial frame is included this time
+    x = [i for i in range(1913,1913+terms+1)]
+    try:
+        assert(len(x) == len(total_supply_si))
+    except AssertionError:
+        print("Assert Error:")
+        print(len(x), len(total_supply_si))
+        print(df_si)
+        exit()
+    dy1 = np.gradient(total_supply_ubi, x)
+    dy2 = np.gradient(total_supply_si, x)
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, dy1, color="cyan", label="deriv. const.div"
+    )
+    plt.plot(
+        x, dy2, color="red", label="deriv. apy"
+    )
+    plt.axvline(x=1960, color='yellow', linestyle='--')
+    plt.axvline(x=1940, color='orange', linestyle='--')
+    plt.title("Change in Supply of Money")
+    plt.legend()
+    plt.xlabel("Year")
+    plt.ylabel("Rate of Inflation")
+    plt.savefig("inflation-ubi-vs-5apy3.png")
+    plt.close()
+    # plt.show()
+
+
+def draw_3ubi_3apy(terms=50):
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
+
+    df = get_balances_over_time (
+        participants, balances, ubi(balances, terms=terms)
+    )
+    total_supply_ubi = calc_total_supply(df)
+
+    df_si = get_balances_over_time (
+        participants, balances, 
+        compounding_interest(balances, terms=terms)
+    )
+    total_supply_si = calc_total_supply(df_si)
+    # + 1 because the initial frame is included this time
+    x = [i for i in range(0,terms+1)]
+    assert(len(x) == len(total_supply_si))
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, total_supply_ubi, color="cyan", label="dividend = 10"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            ubi(balances, rate=5, terms=terms)
+        )),
+        color="violet", label="dividend = 5"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            ubi(balances, rate=15, terms=terms)
+        )),
+        color="lightgreen", label="dividend = 15"
+    )
+    plt.plot(
+        x, total_supply_si, color="red", label="apy = 0.05"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            compounding_interest(balances, rate=0.04, terms=terms)
+        )),
+        color="orange", label="apy = 0.04"
+    )
+    plt.plot(
+        x,
+        calc_total_supply(get_balances_over_time(
+            participants, 
+            balances, 
+            compounding_interest(balances, rate=0.03, terms=terms),
+        )),
+        color="yellow", label="apy = 0.03"
+    )
+    plt.title("Supply of Money Over Time")
+    plt.legend()
+    plt.xlabel("Terms")
+    plt.ylabel("Total Currency")
+    plt.savefig("inflation-ubi-vs-apy2.png")
+    plt.close()
+    # plt.show()
+
+def visualize_inflation(df, title="Inflation", filename="inflation.png"):
+    supply_over_time = calc_total_supply(df)
+    time_span = len(supply_over_time)
+    x = [i for i in range(time_span)]
+    assert(len(x) == time_span)
+
+    plt.style.use('dark_background')
+    plt.plot(
+        x, supply_over_time, color="red")
+    plt.title(title)
+    plt.xlabel("Terms")
+    plt.ylabel("Total Currency")
+    plt.savefig(filename)
+    plt.close()
+
+def visualize_wealth_dist(df, title="Wealth Distribution", filename="wealth-distribution.png"):
+    participants = df["name"]
+    shares = []
+    for key, data in df.items():
+        if key == "name":
+            continue
+        if key == "initial":
+            continue
+        shares.append(calc_share_of_wealth(data))
+    sow = pd.DataFrame(shares)
     # print(sow.keys())
+    terms = sow.shape[0] + 1
     x = [i for i in range(1,terms)]
+    print(len(x))
+    print(len(shares))
     assert(len(x) == len(shares))
 
     plt.style.use('dark_background')
@@ -127,57 +428,164 @@ def visualize_ubi(terms=25):
         y=0.33, color='violet', linestyle='--', label="0.33"
     )
 
-    plt.title("Change in Wealth Distribution Under UBI")
+    plt.title(title)
     plt.legend()
     plt.xlabel("Terms")
     plt.ylabel("Share of Wealth")
-    plt.savefig("ubi-wealth-distribution.png")
+    plt.savefig(filename)
     plt.close()
-    # plt.show()
-    return
 
-visualize_ubi(terms=50)
+def draw_simple_mining_demo():
-
-def calc_total_supply(df):
-    total = []
-    for key, data in df.items():
-        if key == "name":
-            continue
-        total.append(sum(data))
-    return total
-
-def visualize_inflation(terms=50):
     participants = ["Alice", "Bob", "Charlie"]
     balances = [100,40,20]
-
+    m = get_balances_over_time(
-    df = get_balances_over_time (
+        participants, balances, 
-        participants, balances, ubi,
+        mining(balances, halving_frequency=1, terms=10)
-        terms = terms
     )
-    total_supply_ubi = calc_total_supply(df)
+    print(m)
+    print(m.shape[1])
+    visualize_inflation(m, title="Inflation Given block_reward = 10 and Halving Every Term", filename="inflation-pow.png")
+    # print(m.iloc[:,:5].to_markdown())
+    visualize_wealth_dist(m, filename="wealth-distribution-pow.png")
 
-    df_si = get_balances_over_time (
+def trade(players, balances, spend_limit=0.10):
-        participants, balances, compounding_interest,
+    assert(len(players) == len(balances))
-        terms = terms
+    PRICE_FLOOR = 1
+    pl_idx = len(players) - 1
+    #  randomly pick two players that will trade
+    buyer = 0
+    seller = 0
+    while buyer == seller:
+    # reroll until the two arent the same
+        buyer = random.choice([i for i in range(0, pl_idx)])
+        seller = random.choice([i for i in range(0, pl_idx)])
+    price = random.uniform(
+        PRICE_FLOOR, spend_limit * balances[buyer]
     )
-    total_supply_si = calc_total_supply(df_si)
+    balances[buyer] -= price
-    # + 1 because the initial frame is included this time
+    balances[seller] += price
-    x = [i for i in range(1,terms+1)]
+    # print(balances[buyer], balances[seller])
-    assert(len(x) == len(total_supply_si))
+    return
 
-    plt.style.use('dark_background')
+class Sim:
-    plt.plot(
+    def __init__(self, players, balances, terms=50, starting_amount=0):
-        x, total_supply_ubi, color="cyan", label="UBI (n=3, du=10)"
+        self.players = players.copy()
-    )
+        self.balances = balances.copy()
-    plt.plot(
+        self.terms = terms
-        x, total_supply_si, color="red", label="Debt (APY=5%)"
-    )
-    plt.title("Money Supply over Time: UBI vs Compounding Interest")
-    plt.legend()
-    plt.xlabel("Terms")
-    plt.ylabel("Total Currency")
-    plt.savefig("inflation-ubi-vs-5apy.png")
-    plt.close()
-    # plt.show()
 
-visualize_inflation(terms=75)
+        self.ADD_NEW_PPL_EVERY = 10
+        self.NEW_PPL_START_WITH_BALANCE = starting_amount
+        self.IS_TRADING = True
+
+        self.IS_HALVING = False
+        self.HALVING_EVERY = 5
+
+        self.TITLE = str()
+        self.events = []
+
+    def add_player(self, 
+                   balances, name="Player %i", starting_amount=0):
+        name = name % (len(self.players) + 1)
+        self.players.append(name)
+        balances.append(starting_amount)
+        try:
+            assert(len(self.players) == len(balances))
+        except AssertionError as e:
+            print(e)
+            print(len(self.players))
+            print(len(balances))
+            sys.exit()
+
+    def run(self, update):
+        ADD_NEW_PPL_EVERY = self.ADD_NEW_PPL_EVERY
+        INIT_AMT = self.NEW_PPL_START_WITH_BALANCE
+        current_balances = self.balances
+        result = list()
+        
+        dividend = 10            
+
+        i = 0
+        while i < self.terms:
+            # add new player?
+            if i > 0 \
+            and ADD_NEW_PPL_EVERY > 0 \
+            and i % ADD_NEW_PPL_EVERY == 0:
+                self.add_player(
+                    current_balances, starting_amount=INIT_AMT)
+            new_bal = []
+            # make players trade if an
+            if self.IS_TRADING:
+                trade(self.players, current_balances)
+            for balance in current_balances:
+                if not self.IS_HALVING \
+                or update == mini_apy:
+                    b = update(balance)
+                else:
+                    b = mini_dividend(balance, dividend=dividend)
+                new_bal.append(b)
+            
+            result.append(new_bal)
+            current_balances = new_bal
+            if i > 0 and i % self.HALVING_EVERY == 0:
+                dividend = dividend/2
+            i += 1
+        return result
+
+    def visualize(self, data, filename=str()):
+        data.insert(0, self.balances)
+        data2 = [calc_share_of_wealth(row) for row in data]
+        df = pd.DataFrame(data2)
+
+        # +1 to offset 0 index
+        x = [i for i in range(1,df.shape[0] + 1)]
+        assert(len(x) == df.shape[0])
+
+        plt.style.use('dark_background')        
+        # each column is the balance over time of an individual
+        for column in df.columns:
+            plt.plot(x, df[column], label=self.players[column] )
+        plt.xlabel("Terms")
+        plt.ylabel("Share of Wealth")
+        plt.title(self.TITLE)
+        # plt.legend()
+        if filename != str():
+            plt.savefig(filename)
+        else:
+            plt.show()
+        plt.close()
+
+def mini_apy(balance, rate=0.05):
+    return balance + (balance * rate)
+
+def mini_dividend(balance, dividend = 10):
+    return balance + dividend
+
+if __name__ == "__main__":
+
+    # illustrate_share_of_wealth()
+    # visualize_ubi(terms=50)
+    # draw_apy_inflation(terms=75)
+    # draw_apy_inflation2(terms=75, linear_label="dividend = 10")
+    # draw_apy_inflation3(terms=75, linear_label="dividend = 10")
+    # draw_3ubi_3apy(terms=75)
+    # 
+    # draw_simple_mining_demo()
+
+    participants = ["Alice", "Bob", "Charlie"]
+    balances = [100,40,20]
+    s = Sim(participants, balances)
+    result = s.run(update=mini_dividend)
+    s.TITLE = "Constant dividend with new players joining and trading"
+    # s.visualize(result, "const-new-players-trade.png")
+
+    t = Sim(participants, balances)
+    result2 = t.run(update=mini_apy)
+    t.TITLE = "APY with new people joining and trading"
+    # t.visualize(result2, "apy-new-playrs-trade.png")
+
+    u = Sim(participants, balances)
+    u.IS_HALVING = True
+    result3 = u.run(update=mini_dividend)
+    # print(pd.DataFrame(result3))
+    u.TITLE = "Halving dividend when new users mine and trade"
+    u.visualize(result3, filename="halving-dividend-new-users-trade.png")